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arXiv 2022年

作者： Huang, Yiyan Leung, Cheuk Hang Wu, Qi Yan, Xing Ma, Shumin Yuan, Zhiri Wang, Dongdong Huang, Zhixiang School of Data Science The City University of Hong Kong Hong Kong Institute of Statistics and Big Data Renmin University of China China Division of Science and Technology BNU-HKBU United International College China JD Digits-CityU Joint Lab The City University of Hong Kong Hong Kong JD Digits China Guangdong Provincial Key Laboratory of Interdisciplinary Research and Application for Data Science BNU-HKBU United International College China

Many practical decision-making problems in economics and healthcare seek to estimate the average treatment effect (ATE) from observational data. The Double/Debiased Machine Learning (DML) is one of the prevalent methods to estimate ATE in the observational study. However, the DML estimators can suffer an error-compounding issue and even give an extreme estimate when the propensity scores are misspecified or very close to 0 or 1. Previous studies have overcome this issue through some empirical tricks such as propensity score trimming, yet none of the existing literature solves this problem from a theoretical standpoint. In this paper, we propose a Robust Causal Learning (RCL) method to offset the deficiencies of the DML estimators. Theoretically, the RCL estimators i) are as consistent and doubly robust as the DML estimators, and ii) can get rid of the error-compounding issue. Empirically, the comprehensive experiments show that i) the RCL estimators give more stable estimations of the causal parameters than the DML estimators, and ii) the RCL estimators outperform the traditional estimators and their variants when applying different machine learning models on both simulation and benchmark datasets. Copyright © 2022, The Authors. All rights reserved.

关键词： Machine learning

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Use of IT in project-based learning applied to the subject surveying in civil engineering 11th

Use of IT in project-based learning applied to the subject s...

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11th International Conference on EUropean Transnational Education, ICEUTE 2020

作者： Mitxelena-Hoyos, Oihana Amaro-Mellado, José Lázaro Martínez-Álvarez, Francisco Department of Graphic Expression and Engineering Project University of the Basque Country Donostia Spain Cantabria-Basque Country Division National Geographic Institute of Spain Donostia Spain Department of Graphic Engineering University of Seville Seville Spain Andalusia Division National Geographic Institute of Spain Seville Spain Data Science & Big Data Lab Pablo de Olavide University Seville41013 Spain

ISBN: (纸本)9783030577988

This work describes the design and implementation of the subject of surveying under the Project-based Learning method. The modernization of teaching-learning and the new requirements of society to our graduates, force us to move towards a new style of higher education. In the case of topography, which is a transversal science closely related to the various skills of the degree, the learning conveyed by a project provides verisimilitude and depth of the knowledge acquired. Given the previous experiences, it is expected that better marks and performance are reached by the students. © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2021.

关键词： Surveys

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M3CS: Multi-Target Masked Point Modeling with Learnable Codebook and Siamese Decoders

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IEEE Transactions on Circuits and Systems for Video Technology 2025年

作者： Qiu, Qibo Yang, Honghui Jiang, Jian Zhang, Shun Ying, Haochao Gao, Haiming Wang, Wenxiao He, Xiaofei Zhejiang University State Key Lab of CAD&CG China Research & Innovation Institute China Zhejiang Lab China School of Public Health Zhejiang University Department of Big Data in Health Science China School of Medicine Zhejiang University Department of Breast Surgery and Oncology Second Affiliated Hospital China Zhejiang University ZJU-Hangzhou Global Scientific and Technological Innovation Center China Zhejiang University School of Software Technology China

Masked point modeling has become a promising scheme of self-supervised pre-training for point clouds. Existing methods reconstruct either the masked points or related features as the objective of pre-training. However, considering the diversity of downstream tasks, it is necessary for the model to have both low- and high-level representation modeling capabilities during pre-training. It enables the capture of both geometric details and semantic contexts. To this end, M3CS is proposed to endow the model with the above abilities. Specifically, with the masked point cloud as input, M3CS introduces two decoders to reconstruct masked representations and the masked points simultaneously. While an extra decoder doubles parameters for the decoding process and may lead to overfitting, we propose siamese decoders to keep the number of learnable parameters unchanged. Further, we propose an online codebook projecting continuous tokens into discrete ones before reconstructing masked points. In such a way, we can compel the decoder to take effect through the combinations of tokens rather than remembering each token. Comprehensive experiments show that M3CS achieves superior performance across both classification and segmentation tasks, outperforming existing methods that are also single-modality and single-scale. © 2025 IEEE. All rights reserved.

关键词： Self-supervised learning

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Reasoning over semantic-level graph for fact checking 58

Reasoning over semantic-level graph for fact checking

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58th Annual Meeting of the Association for Computational Linguistics, ACL 2020

作者： Zhong, Wanjun Xu, Jingjing Tang, Duyu Xu, Zenan Duan, Nan Zhou, Ming Wang, Jiahai Yin, Jian The School of Data and Computer Science Sun Yat-Sen University Guangdong Key Laboratory of Big Data Analysis and Processing Guangzhou China Microsoft Research MOE Key Lab of Computational Linguistics Peking University

ISBN: (纸本)9781952148255

Fact checking is a challenging task because verifying the truthfulness of a claim requires reasoning about multiple retrievable evidence. In this work, we present a method suitable for reasoning about the semantic-level structure of evidence. Unlike most previous works, which typically represent evidence sentences with either string concatenation or fusing the features of isolated evidence sentences, our approach operates on rich semantic structures of evidence obtained by semantic role labeling. We propose two mechanisms to exploit the structure of evidence while leveraging the advances of pre-trained models like BERT, GPT or XLNet. Specifically, using XLNet as the backbone, we first utilize the graph structure to re-define the relative distances of words, with the intuition that semantically related words should have short distances. Then, we adopt graph convolutional network and graph attention network to propagate and aggregate information from neighboring nodes on the graph. We evaluate our system on FEVER, a benchmark dataset for fact checking, and find that rich structural information is helpful and both our graph-based mechanisms improve the accuracy. Our model is the state-of-the-art system in terms of both official evaluation metrics, namely claim verification accuracy and FEVER score. © 2020 Association for Computational Linguistics

关键词： Semantics

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Multiclass Diabetic Retinopathy Classification of Eye Fundus Images Small datasets Performance Improvement - A Neuroevolution Approach 49

Multiclass Diabetic Retinopathy Classification of Eye Fundus...

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49th Latin American Computing Conference, CLEI 2023

作者： Vazquez Noguera, Jose Luis Mello-Roman, Julio Cesar Pinto-Roa, Diego P. Gomez, Santiago Ayala, Jordan Aquino-Britez, Diego Gardel-Sotomayor, Pedro Esteban Garcia-Torres, Miguel Facon, Jacques Castillo Benitez, Veronica E. Matto, Ingrid Castro Perez-Estigarribia, Pastor Universidad Nacional de Asunción Facultad Politécnica San Lorenzo Paraguay Universidad Americana Department of Computer Engineer Asunción Paraguay Universidad Católica Nuestra Señora de la Asunción Facultad de Ciencias y Tecnología Hernandarías Paraguay Data Science and Big Data Lab Universidad Pablo de Olavide Seville Spain Universidade Federal Do Espírito Santo Department of Computer and Electronics São Mateus Brazil Hospital de Clí Nicas Universidad Nacional de Asunción Facultad de Ciencias Mé Dicas Department of Ophthalmology San Lorenzo Paraguay Universidad Sudamericana Facultad de Medicina Pedro Juan Caballero Paraguay

ISBN: (纸本)9798350318876

Diabetic retinopathy is an eye complication of a widespread disease named diabetes mellitus. The most widely used method for diagnosing diabetic retinopathy is the analysis of retinal fundus images obtained by retinography. Deep Learning-based methods have shown promising results as a diagnostic tool for diabetic retinopathy, achieving, in some cases, performance close to the human inspection of images. However, the performance of these methods relies heavily on fine-tuning the algorithm hyperparameters and big data sets. In this work, we propose training a Deep Learning network with evolutionary algorithms to classify three stages of Diabetic Retinopathy: i) no sign of diabetic retinopathy, ii) Non-proliferative dia-betic retinopathy, and iii) proliferative diabetic retinopathy. We propose a neuroevolution methodology for selecting the most efficient Deep Learning model. The results of the neuroevolution methodology were improved by including Simulated Annealing strategies, Population Reinitialization, and ensembles. With high accuracy, sensitivity, specificity, and kappa index rates of 0.889, 0.889, 0.951, and 0.822, respectively, in the best case found, the experiments show that our neuroevolution methodology for selecting the Deep Learning model hyperparameters is a competitive alternative for training deep neural networks to classify three stages of diabetic retinopathy even with a small data set. © 2023 IEEE.

关键词： Evolutionary algorithms

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Integrated Sensing, Computation, and Communication for UAV-assisted Federated Edge Learning

arXiv

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arXiv 2023年

作者： Tang, Yao Zhu, Guangxu Xu, Wei Cheung, Man Hon Lok, Tat-Ming Cui, Shuguang Hong Kong Shenzhen Research Institute of Big Data Shenzhen China The National Mobile Communications Research Lab Southeast University Nanjing210096 China The Department of Computer Science City University of Hong Kong Hong Kong Shenzhen China Peng Cheng Laboratory China

Federated edge learning (FEEL) enables privacy-preserving model training through periodic communication between edge devices and the server. Unmanned Aerial Vehicle (UAV)-mounted edge devices are particularly advantageous for FEEL due to their flexibility and mobility in efficient data collection. In UAV-assisted FEEL, sensing, computation, and communication are coupled and compete for limited onboard resources, and UAV deployment also affects sensing and communication performance. Therefore, the joint design of UAV deployment and resource allocation is crucial to achieving the optimal training performance. In this paper, we address the problem of joint UAV deployment design and resource allocation for FEEL via a concrete case study of human motion recognition based on wireless sensing. We first analyze the impact of UAV deployment on the sensing quality and identify a threshold value for the sensing elevation angle that guarantees a satisfactory quality of data samples. Due to the non-ideal sensing channels, we consider the probabilistic sensing model, where the successful sensing probability of each UAV is determined by its position. Then, we derive the upper bound of the FEEL training loss as a function of the sensing probability. Theoretical results suggest that the convergence rate can be improved if UAVs have a uniform successful sensing probability. Based on this analysis, we formulate a training time minimization problem by jointly optimizing UAV deployment, integrated sensing, computation, and communication (ISCC) resources under a desirable optimality gap constraint. To solve this challenging mixed-integer non-convex problem, we apply the alternating optimization technique, and propose the bandwidth, batch size, and position optimization (BBPO) scheme to optimize these three decision variables alternately. Simulation results demonstrate that our BBPO scheme outperforms other baseline schemes regarding convergence rate and testing accuracy. Copyright © 2023

关键词： Unmanned aerial vehicles (UAV)

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Adaptive Kalman-Based Hybrid Car Following Strategy Using Td3 and Cacc

SSRN

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SSRN 2024年

作者： Zheng, Yuqi Yan, Ruidong Jia, Bin Jiang, Rui Tapus, Adriana Chen, Xiaojing Zheng, Shiteng Ying, Shang Key Laboratory of Transport Industry of Big Data Application Technologies for Comprehensive Transport School of Systems Science Beijing Jiaotong University Beijing100044 China Autonomous Systems and Robotics Lab ENSTA Paris Institute Polytechnique de Paris Palaiseau91120 France School of Economics and Management Beihang University Beijing100044 China

In autonomous driving, the hybrid strategy of deep reinforcement learning and cooperative adaptive cruise control (CACC) can fully utilize the advantages of both algorithms and significantly improve the performance of car following. However, the conventional hybrid car following strategy (HCFS) based on fixed coefficient can hardly adapt to mixed traffic flow scenarios. As a result, HCFS may decrease car following performance and even increase collision risk. To address this problem, an adaptive Kalman-based HCFS (AK-HCFS) is proposed by fusing CACC and Twin Delayed Deep Deterministic Policy Gradient (TD3) algorithms. Different from conventional HCFS based on fixed coefficient, the adaptive Kalman gain H, adjusting to complex traffic scenarios, is derived from multi-timestep predictions and Monte Carlo Tree Search algorithm. Finally, simulation results with 4157745 timesteps indicate that, compared with TD3 and HCFS, AK-HCFS can substantially enhance the safety of car following in mixed traffic flow without decreasing the comfort and efficiency. © 2024, The Authors. All rights reserved.

关键词： Reinforcement learning

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Equivariant spatio-temporal attentive graph networks to simulate physical dynamics 23

Equivariant spatio-temporal attentive graph networks to simu...

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Proceedings of the 37th International Conference on Neural Information Processing Systems

作者： Liming Wu Zhichao Hou Jirui Yuan Yu Rong Wenbing Huang Gaoling School of Artificial Intelligence Renmin University of China and Beijing Key Laboratory of Big Data Management and Analysis Methods Beijing China Department of Computer Science North Carolina State University Institute for AI Industry Research (AIR) Tsinghua University Tencent AI Lab

Learning to represent and simulate the dynamics of physical systems is a crucial yet challenging task. Existing equivariant Graph Neural Network (GNN) based methods have encapsulated the symmetry of physics, e.g., translations, rotations, etc, leading to better generalization ability. Nevertheless, their frame-to-frame formulation of the task overlooks the non-Markov property mainly incurred by unobserved dynamics in the environment. In this paper, we reformulate dynamics simulation as a spatio-temporal prediction task, by employing the trajectory in the past period to recover the Non-Markovian interactions. We propose Equivariant Spatio-Temporal Attentive Graph Networks (ESTAG), an equivariant version of spatio-temporal GNNs, to fulfill our purpose. At its core, we design a novel Equivariant Discrete Fourier Transform (EDFT) to extract periodic patterns from the history frames, and then construct an Equivariant Spatial Module (ESM) to accomplish spatial message passing, and an Equivariant Temporal Module (ETM) with the forward attention and equivariant pooling mechanisms to aggregate temporal message. We evaluate our model on three real datasets corresponding to the molecular-, protein- and macro-level. Experimental results verify the effectiveness of ESTAG compared to typical spatio-temporal GNNs and equivariant GNNs.

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Boosting Fine-Grained Visual Anomaly Detection with Coarse-Knowledge-Aware Adversarial Learning

arXiv

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arXiv 2024年

作者： Fang, Qingqing Su, Qinliang Lv, Wenxi Xu, Wenchao Yu, Jianxing School of Computer Science and Engineering Sun Yat-sen University Guangzhou China Guangdong Key Laboratory of Big Data Analysis and Processing Guangzhou China Department of Computing The Hong Kong Polytechnic University Hong Kong School of Artificial Intelligence Sun Yat-sen University Guangdong China Pazhou Lab Guangzhou510330 China

Many unsupervised visual anomaly detection methods train an auto-encoder to reconstruct normal samples and then leverage the reconstruction error map to detect and localize the anomalies. However, due to the powerful modeling and generalization ability of neural networks, some anomalies can also be well reconstructed, resulting in unsatisfactory detection and localization accuracy. In this paper, a small coarsely-labeled anomaly dataset is first collected. Then, a coarse-knowledge-aware adversarial learning method is developed to align the distribution of reconstructed features with that of normal features. The alignment can effectively suppress the auto-encoder’s reconstruction ability on anomalies and thus improve the detection accuracy. Considering that anomalies often only occupy very small areas in anomalous images, a patch-level adversarial learning strategy is further developed. Although no patch-level anomalous information is available, we rigorously prove that by simply viewing any patch features from anomalous images as anomalies, the proposed knowledge-aware method can also align the distribution of reconstructed patch features with the normal ones. Experimental results on four medical datasets and two industrial datasets demonstrate the effectiveness of our method in improving the detection and localization performance. Copyright © 2024, The Authors. All rights reserved.

关键词： Adversarial machine learning

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REASONER: An Explainable Recommendation dataset with Multi-aspect Real User labeled Ground Truths Towards more Measurable Explainable Recommendation

arXiv

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arXiv 2023年

作者： Chen, Xu Zhang, Jingsen Wang, Lei Dai, Quanyu Dong, Zhenhua Tang, Ruiming Zhang, Rui Chen, Li Wen, Ji-Rong Beijing Key Laboratory of Big Data Management and Analysis Methods Beijing China Gaoling School of Artificial Intelligence Renmin University of China Beijing China Huawei Noah’s Ark Lab China Department of Computer Science Hong Kong Baptist University Hong Kong

Explainable recommendation has attracted much attention from the industry and academic communities. It has shown great potential for improving the recommendation persuasiveness, informativeness and user satisfaction. Despite a lot of promising explainable recommender models have been proposed in the past few years, the evaluation strategies of these models suffer from several limitations. For example, the explanation ground truths are not labeled by real users, the explanations are mostly evaluated based on only one aspect and the evaluation strategies can be hard to unify. To alleviate the above problems, we propose to build an explainable recommendation dataset with multi-aspect real user labeled ground truths. In specific, we firstly develop a video recommendation platform, where a series of questions around the recommendation explainability are carefully designed. Then, we recruit about 3000 users with different backgrounds to use the system, and collect their behaviors and feedback to our questions. In this paper, we detail the construction process of our dataset and also provide extensive analysis on its characteristics. In addition, we develop a library, where ten well-known explainable recommender models are implemented in a unified framework. Based on this library, we build several benchmarks for different explainable recommendation tasks. At last, we present many new opportunities brought by our dataset, which are expected to shed some new lights to the explainable recommendation field. Our dataset, library and the related documents have been released at https://***/. Copyright © 2023, The Authors. All rights reserved.

关键词： Machine learning

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