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2024 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2024

作者： Dai, Mingzhe Shi, Xiaoshuang Zhu, Xiaofeng Pan, Tingrui Li, Kang University of Science and Technology of China School of Software Engineering Hefei China West China Hospital of Sichuan University West China Biomedical Big Data Center Chengdu China University of Electronic Science and Technology of China School of Computer Science and Engineering Chengdu China Sichuan Artificial Intelligence Research Institute Yibin China University of Science and Technology of China Suzhou Institute for Advanced Research Suzhou China Shanghai Artificial Intelligence Laboratory Shanghai China

ISBN: (纸本)9798350386226

Deep multiple instance learning (MIL) has attracted considerable attention in medical image analysis, since it only requires image-level labels for model training without using fine-grained (or patch) annotations. Unfortunately, MIL-based methods might lose some significant patch features. Although pseudo-label-based methods, which assign a pre-defined label to each patch, can explore more patch-level features, they might bring label noise and make the patch-level features lose diversity, thereby possibly restricting the model performance. To overcome this issue, we propose a novel gradient-based patch target generation (PTG) module to dynamically produce a feature vector for each patch as its target. Additionally, based on the PTG module, we propose a patch-target guided dual-branch deep MIL framework for 3D MRI data analysis, where both the two branches consist of a CNN model to extract patch-level features, an attention module to interpret the significance of patches, and a bag-level classifier, while the second branch also contains the PTG module to generate patch targets of patches. Moreover, the two branches are alternatively updated in our framework, resulting in a bi-level optimization problem, and thus we design a bi-level optimization algorithm to solve our proposed objective function. Extensive experiments demonstrate the superior classification and interpretation performance of the proposed framework over recent state-of-the-art methods. Codes are available at https://***/daimz1213/mil2024. © 2024 IEEE.

关键词： Image annotation

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Do We Really Need GCNs in Traffic Forecasting? A Graph-Less Pure-MLP Architecture 25

Do We Really Need GCNs in Traffic Forecasting? A Graph-Less ...

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Companion Proceedings of the ACM on Web Conference 2025

作者： Yudong Zhang Xu Wang Xuan Yu Kuo Yang Zhengyang Zhou Yang Wang School of Artificial Intelligence and Data Science University of Science and Technology of China Hefei China School of Software Engineering University of Science and Technology of China Hefei China and Suzhou Institute for Advanced Research University of Science and Technology of China Suzhou China School of Software Engineering University of Science and Technology of China Hefei China

ISBN: (纸本)9798400713316

Traffic flow forecasting, as a fundamental task of intelligent transportation systems driven by the Web, remains challenging due to the complex spatial and temporal dependencies between different roads. Considering the inherent graph structure of road networks, recent works capture the spatial dependencies by utilizing graph convolutional networks (GCNs) and have devoted great efforts to enhancing GCNs. However, graph convolution, as the core of GCNs, has quadratic time and space complexity due to the internal matrix multiplication operation on the adjacency matrix, which prevents existing works from being deployed on huge road networks. In this paper, we doubt the necessity of graph convolution in capturing spatial dependencies and find that the aggregating operation in graph convolution can be replaced by Multi-Layer Perceptron (MLP) with layer normalization in traffic forecasting. Inspired by the emerging MLP models on other deep learning tasks, we further propose a pure-MLP traffic forecasting model without the dependence on graph convolution. Experiments on several real-world traffic datasets show that our pure-MLP model is more efficient and has competitive performances compared to state-of-the-art works.

关键词： intelligent transportation systems

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VAE Latent Space Quantization Methods for UAVs Video Stream 8

VAE Latent Space Quantization Methods for UAVs Video Stream

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8th International Conference on Information, Control, and Communication Technologies, ICCT 2024

作者： Chenskiy, Alexander Berezkin, Alexander Vivchar, Roman Kirichek, Ruslan Kukunin, Dmitry Bonch-Bruevich Saint-Petersburg University of Telecommunications Advanced Projects and Developments Department Saint-Petersburg Russia Bonch-Bruevich Saint-Petersburg University of Telecommunications Software Engineering and Computer Science Department Saint-Petersburg Russia Bonch-Bruevich Saint-Petersburg University of Telecommunications Communication Networks and Data Transmission Department Saint-Petersburg Russia

ISBN: (纸本)9798331517564

In FPV control of unmanned aerial vehicles, video frames are transmitted to the external pilot's station via a down link. Down links utilize mobile and hybrid orbital-terrestrial communication networks. The efficiency increase in their utilization is achieved by reducing the required bandwidth for each FPV-controlled unmanned aircraft. This requires ensuring a high compression rate of video stream frames, for which methods based on the application of variational autoencoders can be used. The principle of their operation is as follows: encoding frames into latent space, quantization and lossless compression. This paper presents new quantization algorithms and investigates the effect of applying different quantization algorithms to compression rate of latent space of VQ-D6 variational autoencoder from Stable Diffusion during further lossless compression using LZMA algorithm. In the result, the most effective quantization is selected to be used together with lossless compression. © 2024 IEEE.

关键词： Unmanned aerial vehicles (UAV)

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MA2-FPN for Tiny Object Detection from Remote Sensing Images 15

MA2-FPN for Tiny Object Detection from Remote Sensing Images

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15th International Congress on Image and Signal Processing, BioMedical engineering and Informatics, CISP-BMEI 2022

作者： Li, Saiwei Tong, Qiang Liu, Xuhong Cui, Zhanqi Liu, Xiulei College of Computer Beijing Information Science and Technology University Beijing China Beijing Information Science and Technology University Beijing Advanced Innovation Center for Materials Genome Engineering Laboratory of Data Science and Information Studies Beijing China

ISBN: (数字)9781665488877

ISBN: (纸本)9781665488877

Tiny object detection has been a challenging topic in computer vision recent years. Moreover, in remote sensing field, smaller and clustered tiny objects make its detection more difficult compared to ground-based images. This makes general detectors fail to achieve good performance when facing tiny objects in remote sensing images. In this paper, we propose a Mask Augmented Attention Feature Pyramid Network(MA2-FPN) to detect tiny objects in remote sensing images, which consists of two modules, Attention Enhancement Module(AEM) and Mask Supervision Module(MSM). Specifically, AEM aggregates tiny target context and spatial feature information by large kernel separable convolutional attention mechanism, and MSM supervises AEM through a segmentation attention loss to aggregate attention information more accurately while suppressing the influence of irrelevant background. Experiments based on the AI-TOD benchmark show that our MA2-FPN achieves state-of-the-art(SOTA) level. © 2022 IEEE.

关键词： Object detection

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CLDG: Contrastive Learning on Dynamic Graphs 39

CLDG: Contrastive Learning on Dynamic Graphs

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39th IEEE International Conference on data engineering, ICDE 2023

作者： Xu, Yiming Shi, Bin Ma, Teng Dong, Bo Zhou, Haoyi Zheng, Qinghua Xi'an Jiaotong University Department of Computer Science and Technology China Xi'an Jiaotong University Shaanxi Provincial Key Laboratory of Big Data Knowledge Engineering China Xi'an Jiaotong University Department of Distance Education China Beihang University School of Software China Beihang University Advanced Innovation Center for Big Data and Brain Computing China

ISBN: (纸本)9798350322279

The graph with complex annotations is the most potent data type, whose constantly evolving motivates further exploration of the unsupervised dynamic graph representation. One of the representative paradigms is graph contrastive learning. It constructs self-supervised signals by maximizing the mutual information between the statistic graph's augmentation views. However, the semantics and labels may change within the augmentation process, causing a significant performance drop in downstream tasks. This drawback becomes greatly magnified on dynamic graphs. To address this problem, we designed a simple yet effective framework named CLDG. Firstly, we elaborate that dynamic graphs have temporal translation invariance at different levels. Then, we proposed a sampling layer to extract the temporally-persistent signals. It will encourage the node to maintain consistent local and global representations, i.e., temporal translation invariance under the timespan views. The extensive experiments demonstrate the effectiveness and efficiency of the method on seven datasets by outperforming eight unsupervised state-of-the-art baselines and showing competitiveness against four semi-supervised methods. Compared with the existing dynamic graph method, the number of model parameters and training time is reduced by an average of 2,001.86 times and 130.31 times on seven datasets, respectively. The code and data are available at: https://***/yimingxu24/CLDG. © 2023 IEEE.

关键词： Graph neural networks

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NC-ALG: Graph-Based Active Learning under Noisy Crowd 40

NC-ALG: Graph-Based Active Learning under Noisy Crowd

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40th IEEE International Conference on data engineering, ICDE 2024

作者： Zhang, Wentao Wang, Yexin You, Zhenbang Li, Yang Cao, Gang Yang, Zhi Cui, Bin Center for Machine Learning Research Peking University China Key Lab of High Confidence Software Technologies Peking University China Institute of Advanced Algorithms Research Shanghai China Institute of Computational Social Science Peking University Qingdao China National Engineering Labratory for Big Data Analytics and Applications China TEG Tencent Inc. Department of Data Platform China Beijing Academy of Artificial Intelligence China

ISBN: (纸本)9798350317152

Graph Neural Networks (GNNs) have achieved great success in various data mining tasks but they heavily rely on a large number of annotated nodes, requiring considerable human efforts. Despite the effectiveness of existing GNN-based Active Learning (AL) methods, they assume that the annotated labels are always correct, which is contradictory to the error-prone labeling process in a practical crowdsourcing environment. Besides, due to this impractical assumption, existing works only focus on optimizing the node selection in AL but neglect optimizing the labeling process. Therefore, we present NC-ALG, the first GNN-based AL framework that optimizes both the node selection and node labeling process under a noisy crowd. For node selection, NC-ALG introduces a new measurement to model influence reliability and an effective influence maximization objective to select nodes. For node labeling, NC-ALG significantly reduces the labeling cost by considering the model-predicted labels and the labels of mirror nodes. To the best of our knowledge, this is the first attempt to consider GNN-based AL under the practical noisy crowd. Empirical studies on public datasets demonstrate that NC-ALG significantly outperforms existing methods in terms labeling efficiency. Notably, it only takes NC-ALG one-third of the labeling budget that the competitive baseline GRAIN needs to achieve an accuracy of 70.7 % on PubMed. © 2024 IEEE.

关键词： Crowdsourcing

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Project Assessment in Offshore software Maintenance Outsourcing Using Deep Extreme Learning Machines

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Computers, Materials & Continua 2023年第1期74卷 1871-1886页

作者： Atif Ikram Masita Abdul Jalil Amir Bin Ngah Saqib Raza Ahmad Salman Khan Yasir Mahmood Nazri Kama Azri Azmi Assad Alzayed Faculty of Ocean Engineering Technology and Informatics University Malaysia TerengganuKuala NerusMalaysia Department of Computer Science&Information Technology Faculty of Information TechnologyThe University of LahoreLahore54000Pakistan Department of Software Engineering Faculty of Information TechnologyThe University of LahoreLahore54000Pakistan Advanced Informatics Department Razak Faculty of Technology and InformaticsUniversiti Teknologi Malaysia54100Kuala LumpurMalaysia Computer Science and Information Systems Department College of Business Studies Public Authority for Applied Education and Training(PAAET)AdailiyahKuwait Department of Computer Science Sharif College of Engineering&TechnologyLahore54000Pakistan

software maintenance is the process of fixing,modifying,and improving software deliverables after they are delivered to the *** can benefit from offshore software maintenance outsourcing(OSMO)in different ways,including time savings,cost savings,and improving the software quality and *** of the hardest challenges for the OSMO vendor is to choose a suitable project among several clients’*** goal of the current study is to recommend a machine learning-based decision support system that OSMO vendors can utilize to forecast or assess the project of OSMO *** projects belong to OSMO vendors,having offices in developing countries while providing services to developed *** the current study,Extreme Learning Machine’s(ELM’s)variant called Deep Extreme Learning Machines(DELMs)is used.A novel dataset consisting of 195 projects data is proposed to train the model and to evaluate the overall efficiency of the proposed *** proposed DELM’s based model evaluations achieved 90.017%training accuracy having a value with 1.412×10^(-3) Root Mean Square Error(RMSE)and 85.772%testing accuracy with 1.569×10^(-3) RMSE with five DELMs hidden *** results express that the suggested model has gained a notable recognition rate in comparison to any previous *** current study also concludes DELMs as the most applicable and useful technique for OSMO client’s project assessment.

关键词： software outsourcing deep extreme learning machine(DELM) machine learning(ML) extreme learning machine assessment

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A Trajectory Prediction Model for Commercial Flight based on Representative Trajectory 5

A Trajectory Prediction Model for Commercial Flight based on...

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5th International Conference on Computer Information science and Application Technology, CISAT 2022

作者： Zhao, Weifan Tong, Qiang Cao, Jianzhi Chen, Yuli Liu, Xiulei Laboratory of Data Science and Information Studies Computer School Beijing Info. Science & Technology University Beijing China Beijing Advanced Innovation Center for Materials Genome Engineering Beijing Information Science and Technology University Beijing China

ISBN: (纸本)9781510660076

In commercial flight trajectory prediction, the models based on deep learning have high versatility and accurate forecast. However, these prediction models have the problem of poor effect when using small data sets. This paper proposes a commercial flight trajectory prediction model based on representative trajectory. This model consists of two parts: Representative trajectory generation and Trajectory prediction. Firstly, our model clusters the commercial flight trajectories on the same route, then generate representative trajectories to represent the route pattern. Secondly, our model uses the Savitzky-Golay filter to eliminate the noise characteristics in these representative trajectories, then combines those with a deviation correction algorithm to predict flight trajectories. By comparing with several other prediction models, the experiment results support that our model performs better in a small data set of the commercial flight trajectory. © 2022 SPIE.

关键词： Clustering algorithms

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FedSTG: Breaking through Spatio-Temporal data Silos with Federated Graph Learning 25

FedSTG: Breaking through Spatio-Temporal Data Silos with Fed...

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Companion Proceedings of the ACM on Web Conference 2025

作者： Yudong Zhang Xu Wang Xuan Yu Kuo Yang Zhengyang Zhou Yang Wang School of Artificial Intelligence and Data Science University of Science and Technology of China Hefei Anhui China School of Software Engineering University of Science and Technology of China Hefei Anhui China and Suzhou Institute for Advanced Research University of Science and Technology of China Suzhou Jiangsu China School of Software Engineering University of Science and Technology of China Hefei Anhui China

ISBN: (纸本)9798400713316

Spatio-temporal graph (STG) forecasting plays a crucial role in various urban computing applications driven by Web systems. However, conventional STG forecasting methods, which rely on centralized data processing, have posed obstacles to real-world applications in numerous data silo scenarios. Given this, some researchers have preliminarily explored incorporating federated learning into STG forecasting. Though promising, the existing methods encounter two critical issues, i.e., dynamics and heterogeneity in STGs. To this end, we propose a novel personalized Federated learning framework for Spatio-Temporal Graph forecasting (called FedSTG), along with an evolutionary graph learning module and a personalized federated aggregation algorithm based on evolving temporal patterns in the framework. Our approach enhances the STG learning capabilities in the federated paradigm, yield addressing the problem of STG forecasting in data silo scenarios. Extensive experiments on four real-world datasets in STG forecasting have been conducted to demonstrate the superiority of our approach.

关键词： data silos

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Analysis and Detection of Fake News Using Machine Learning 3

Analysis and Detection of Fake News Using Machine Learning

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3rd International Conference on Artificial Intelligence For Internet of Things, AIIoT 2024

作者： Sathish Kumar, P. Suthanthiradevi, P. Arul Stephen, C. Ebenezer Abishek, B. Sivakumar, S. Mathiyarasu, M. Computer and Communication Engineering Rajalakshmi institute of technology Chennai124 India Data Science and Business Systems School of Computing SRM institute of Science and Technology Kattankulathur Chennai603203 India Electronics and Communication Engineering Vels Institute of science Technology and Advanced studies Chennai117 India Electronics and Communication Engineering Vel Tech Multi Tech Dr. Rangarajan Dr. Sakunthala Engineering College Avadi Chennai600062 India Electronics and Communication Engineering BIHER Chennai73 India Computer and Communication Engineering Rajalakshmi institute of technology Chennai India

ISBN: (纸本)9798350372120

The Fake news becomes a more critical issue nowadays, it may reduce the trust of the public about the particular information. This project focuses on the advancement of fake news detection model using machine learning. The main goal of this project is to develop, analyze a model that can effectively classify the news articles based on their Subject or contents. Natural preprocessing Algorithm and features such as Stemming, Tokenization, Stop word and TF-IDF Vectorization are used to train the machine learning model. Then the Machine learning Algorithms likeLogistic Regression and Random Forest are used to classifying the dataset which gives 98% accuracy and 99% of accuracy score. For this Project we have taken a dataset of 22413 Real news and 23481 Fake News from the online resources. This model has a potential to reduce the impact of fake news by grant the promotion of accurate and reliable information in a society. © 2024 IEEE.

关键词： Machine learning

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