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40th IEEE International Conference on data Engineering, ICDE 2024

作者： Xie, Yuan Wu, Fan Zhou, Xu Luo, Wensheng Yin, Yifang Zimmermann, Roger Li, Keqin Li, Kenli College of Computer Science and Electronic Engineering Hunan University Hunan China Institute of Data Science National University of Singapore Singapore State University of New York Department of Computer Science New Paltz United States School of Data Science Chinese University of Hong Kong Hong Kong A STAR Singapore

ISBN: (纸本)9798350317152

With the popularity of GPS-equipped smart devices, spatial crowdsourcing (SC) techniques have attracted growing attention in both academia and industry. In existing trajectory-aware task assignment approaches, tasks assigned to a worker may be far apart from each other, resulting in a higher detour cost as the worker needs to deviate from the original trajectory more often than necessary. Motivated by the above observations, we investigate a trajectory-aware task coalition assignment (TCA) problem and prove it to be NP-hard. The goal is to maximize the number of assigned tasks by assigning task coalitions to workers based on their preferred trajectories. To tackle the TCA problem, we develop a batch-based three-stage framework consisting of task grouping, planning, and assignment. Extensive experiments on real and synthetic datasets demonstrate the effectiveness and efficiency of the proposed algorithms. © 2024 IEEE.

关键词： Trajectories

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Hierarchical Encoder-Decoder with Addressable Memory Network for Diagnosis Prediction 28th

Hierarchical Encoder-Decoder with Addressable Memory Networ...

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28th International Conference on database Systems for Advanced Applications, DASFAA 2023

作者： Wang, Mingxia Xiong, Yun Zhang, Yao Yu, Philip S. Zhu, Yangyong Shanghai Key Laboratory of Data Science School of Computer Science Fudan University Shanghai China Department of Computer Science University of Illinois Chicago Chicago United States

ISBN: (纸本)9783031306778

Deep learning methods have demonstrated success in diagnosis prediction on Electronic Health Records (EHRs). Early attempts utilize sequential models to encode patient historical records, but they lack the ability to identify critical diseases for patient health conditions. Besides, some works focus on the hierarchical structure of EHR data during learning patient representations, but neglect it in diagnosis prediction process, which leads to insufficient utilization of hierarchical information. To tackle these challenges, we propose a new Hierarchical Encoder-Decoder with Addressable Memory Network for Diagnosis Prediction named HAMNet. Specifically, we employ a hierarchical encoder-decoder framework to utilize the hierarchical structure of historical visits both during representation learning and diagnosis prediction. Furthermore, we propose the addressable memory network to distinguish core diseases towards patient health status at next visit through the well-designed addressing mechanism. It employs feed-forward layers to build the memory network, which can automatically update the memory module through gradient backpropagation without explicit read/write operations. Finally, we evaluate the performance of HAMNet on a large real-world EHR dataset MIMIC-III, and it achieves the state-of-the-art performance compared with baselines. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Forecasting

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A Semantic-Driven Generative Information Extraction Framework Oriented to Patent Text 21

A Semantic-Driven Generative Information Extraction Framewor...

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21st IEEE International Symposium on Parallel and Distributed Processing with Applications, 13th IEEE International Conference on Big data and Cloud Computing, 16th IEEE International Conference on Social Computing and Networking and 13th International Conference on Sustainable Computing and Communications, ISPA/BDCloud/SocialCom/SustainCom 2023

作者： Zhao, Yushan Wang, Tengke Zhang, Shunxiang Anhui University of Science and Technology School of Mathematics and Big Data Huainan China Anhui University of Science and Technology School of Computer Science and Engineering Huainan China

ISBN: (纸本)9798350329223

Generative information extraction for patent text relies on a text-to-structure encoder-decoder framework, facilitating the automatic construction of patent knowledge bases. Existing models challenge to capture the long-range semantic dependencies due to the decentralized semantic context within the patent text, diminishing the accuracy of information extraction. In this work, we present a Semantic-driven Generative Information Extraction (SGIE) framework oriented to patent texts by enhancing and associating semantic context, leading to an improved information extraction performance. Information networks obtained from the pre-trained language model are used as information representations in the encoder layer, and based on an AMR (Abstract Meaning Representation) parser combined with patent schema knowledge, the patent-AMR graphs are created and viewed as semantic representations. The semantic-enhanced information representations are acquired by alignment from the same level of semantic and information representations, so then the global information representation is gained by gated fusing all semantic-enhanced information representations. A patent scheme knowledge-constrained decoder is employed beam search to generate information based on the semantic order decided, so patent technical information is extracted. Experimental results show that the proposed model is highly accurate and efficient in patent texts and is promising. © 2023 IEEE.

关键词： Semantics

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Flow-Guided Deformable Alignment Network with Self-Supervision for Video Inpainting 48

Flow-Guided Deformable Alignment Network with Self-Supervisi...

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48th IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2023

作者： Wu, Zhiliang Zhang, Kang Sun, Changchang Xuan, Hanyu Yan, Yan Nanjing University of Science and Technology School of Computer Science and Engineering China Illinois Institute of Technology Department of Computer Science United States Anhui University School of Big Data and Statistics China

ISBN: (纸本)9781728163277

Video inpainting aims to utilize plausible contents to fill missing regions in the video. State-of-the-art video inpainting methods typically generate the missing contents of the target frame (current frame) by aggregating the temporal information of reference frames (neighboring frames) aligned using deformable convolution. However, these deformable convolution alignment networks often suffer from offset overflow during training, resulting in unsatisfactory alignment, thereby obtaining compromised inpainting performance. In this paper, we propose a self-supervised Flow-Guided Deformable Alignment (FGDA) network for aligning reference frames at the feature level. FGDA computes the residual of the optical flow as the offsets. This design can effectively reduce the burden of offsets learning, thereby avoiding offset overflow. Furthermore, a gradient-weighted reconstruction loss for supervised completed frame reconstruction is designed, which can use the gradients in all directions of the video frame to emphasize the difficultly reconstructed texture regions, so that detail textures get more attention during training. Experiments show that FGDA-based video inpainting model trained with gradient-weighted reconstruction loss outperforms the state-of-the-art by a significant margin in terms of PSNR and SSIM with relative improvements of 6.2% and 2.1%, respectively. © 2023 IEEE.

关键词： Alignment

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A multi-agents visual federated learning framework for underground coal mines 24

A multi-agents visual federated learning framework for under...

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7th International Conference on Signal Processing and Machine Learning, SPML 2024

作者： Ji, Yucheng Yuan, Hongyong Liu, Yongqiang Fang, Yudong Chen, Wei Ministry of Emergency Management Big Data Center China School of Safety Science Tsinghua University China School of Computer Science and Technology China University of Mining and Technology China

ISBN: (纸本)9798400717192

The video safety monitoring and analysis is a critical problem in underground coal mines. Due to the complex environment in the underground coal mine and the requirement of perception and decision-making abilities from the coal mine robots, single intelligent agent(robot) is difficult to complete complicated tasks. This paper proposes a framework of federated learning for multiple robots(agents) in underground coal mines to achieve video monitor and analysis tasks. Three federated learning models are raised include asynchronous federation optimization algorithm;visual federation method for heterogeneous image data and coal mine video equipment federated model aggregation. The visual federated learning framework can significantly improve the safety monitoring, accelerate the development of automation in the coal mine production field, and constructing and improving smart mines. © 2024 ACM.

关键词： Risk management

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Robust Multi-Object Tracking Using Vision Sensor with Fine-Grained Cues in Occluded and Dynamic Scenes

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IEEE Sensors Journal 2025年第11期25卷 20547-20560页

作者： Hu, Yaoqi Sun, Jinqiu Jin, Hao Niu, Axi Yan, Qingsen Zhu, Yu Zhang, Yanning School of Computer Science China School of Astronautics China National Engineering Laboratory for Integrated Aero-Space-Ground-Ocean Big Data Application Technology Northwestern Polytechnical University Dongxiang Road Shaanxi Xi'an710129 China

Multi-object tracking (MOT) using vision sensors remains a challenging problem, particularly in dynamic backgrounds and severe occlusions. Existing methods, relying on holistic appearance or spatial cues, fail to capture detailed information within object regions and the background, resulting in inaccurate and inconsistent tracking. To address these issues, we propose a novel Detail-Driven Multi-Object Tracking (DD-MOT) method that revisits and leverages fine-grained cues to discover the dynamics of both object regions and the background, facilitating the recovery and association of trajectories. Specifically, the proposed method consists of three key modules: (i) Points Trajectories Generator, (ii) Camera Motion and Occlusion Compensation, and (iii) Fine- and Coarsegrained Association. The Points Trajectories Generator is responsible for generating fine-grained cues by sampling an initial set of points, generating point trajectories, and refining the initial set of points. The Camera Motion and Occlusion Compensation module utilizes the background and object point trajectories to correct background motion and recover occluded object bounding boxes. Finally, the Fine- and Coarse-grained Association module leverages point trajectory cues to assist in establishing a more effective association strategy, combining both fine-grained and coarse-grained spatial cues from the object bounding boxes. We evaluate our DD-MOT method on several benchmark datasets, including MOT17, MOT20, demonstrating that it consistently outperforms state-of-the-art (SOTA) methods in key metrics such as HOTA (64.2, 62.6), MOTA (80.8, 78.1), and IDF1 (78.8, 77.1). © 2025 IEEE.

关键词： Motion compensation

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Mobility-LLM: Learning Visiting Intentions and Travel Preferences from Human Mobility data with Large Language Models 38

Mobility-LLM: Learning Visiting Intentions and Travel Prefer...

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38th Conference on Neural Information Processing Systems, NeurIPS 2024

作者： Gong, Letian Lin, Yan Zhang, Xinyue Lu, Yiwen Han, Xuedi Liu, Yichen Guo, Shengnan Lin, Youfang Wan, Huaiyu School of Computer Science and Technology Beijing Jiaotong University China Beijing Key Laboratory of Traffic Data Analysis and Mining Beijing China Department of Computer Science Aalborg University Denmark

Location-based services (LBS) have accumulated extensive human mobility data on diverse behaviors through check-in sequences. These sequences offer valuable insights into users' intentions and preferences. Yet, existing models analyzing check-in sequences fail to consider the semantics contained in these sequences, which closely reflect human visiting intentions and travel preferences, leading to an incomplete comprehension. Drawing inspiration from the exceptional semantic understanding and contextual information processing capabilities of large language models (LLMs) across various domains, we present Mobility-LLM, a novel framework that leverages LLMs to analyze check-in sequences for multiple tasks. Since LLMs cannot directly interpret check-ins, we reprogram these sequences to help LLMs comprehensively understand the semantics of human visiting intentions and travel preferences. Specifically, we introduce a visiting intention memory network (VIMN) to capture the visiting intentions at each record, along with a shared pool of human travel preference prompts (HTPP) to guide the LLM in understanding users' travel preferences. These components enhance the model's ability to extract and leverage semantic information from human mobility data effectively. Extensive experiments on four benchmark datasets and three downstream tasks demonstrate that our approach significantly outperforms existing models, underscoring the effectiveness of Mobility-LLM in advancing our understanding of human mobility data within LBS contexts. © 2024 Neural information processing systems foundation. All rights reserved.

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Multi-Modal Fusion with Semantic Supervision for Radiology Report Generation 26

Multi-Modal Fusion with Semantic Supervision for Radiology R...

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26th European Conference on Artificial Intelligence, ECAI 2023

作者： Jia, Xing Xiong, Yun Zhang, Yao Luo, Li Shanghai Key Laboratory of Data Science School of Computer Science Fudan University China School of Public Health Fudan University China

ISBN: (纸本)9781643684369

Radiology report generation, one way of analyzing radiology images, is to generate a textual report automatically for the given image, and it is of great significance to assist diagnosis and alleviate the workload of radiologists. Some report generation methods have been therefore proposed. However, these methods suffer from the problem of low-quality generation, because of the visual and textual bias and training with text similarity oriented objective. To solve this problem, we propose a novel radiology report generation model with multi-modal fusion and semantic supervision, namely MS-Gen. MS-Gen consists of two main components, i.e., the semantic-visual fusion module and the semantic weighted contrastive loss. Specifically, the main idea of the semantic-visual fusion module is to make use of the domain-specific prior knowledge contained in a large pre-trained visual-language model and also the complementary nature between the image and text modalities. Moreover, a novel optimization term, i.e., the semantic weighted contrastive loss, is proposed to guide the optimization process with semantic similarity objective, and further enforce the generated reports with higher clinical accuracy. Extensive experiments conducted on two real datasets of IU X-Ray and MIMIC-CXR demonstrate the effectiveness of MS-Gen. © 2023 The Authors.

关键词： Semantics

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Make Large Language Model a Better Ranker

Make Large Language Model a Better Ranker

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2024 Conference on Empirical Methods in Natural Language Processing, EMNLP 2024

作者： Chao, Wen-Shuo Zheng, Zhi Zhu, Hengshu Liu, Hao The Hong Kong University of Science and Technology Guangzhou China School of Data Science University of Science and Technology of China China Computer Network Information Center Chinese Academy of Sciences China

ISBN: (纸本)9798891761681

Large Language Models (LLMs) demonstrate robust capabilities across various fields, leading to a paradigm shift in LLM-enhanced Recommender System (RS). Research to date focuses on point-wise and pair-wise recommendation paradigms, which are inefficient for LLM-based recommenders due to high computational costs. However, existing list-wise approaches also fall short in ranking tasks due to misalignment between ranking objectives and next-token prediction. Moreover, these LLM-based methods struggle to effectively address the order relation among candidates, particularly given the scale of ratings. To address these challenges, this paper introduces the large language model framework with Aligned Listwise Ranking Objectives (ALRO). ALRO is designed to bridge the gap between the capabilities of LLMs and the nuanced requirements of ranking tasks. Specifically, ALRO employs explicit feedback in a listwise manner by introducing soft lambda loss, a customized adaptation of lambda loss designed for optimizing order relations. This mechanism provides more accurate optimization goals, enhancing the ranking process. Additionally, ALRO incorporates a permutation-sensitive learning mechanism that addresses position bias, a prevalent issue in generative models, without imposing additional computational burdens during inference. Our evaluative studies reveal that ALRO outperforms both existing embedding-based recommendation methods and LLM-based recommendation baselines. © 2024 Association for Computational Linguistics.

关键词： Computational linguistics

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Learning-Efficient Yet Generalizable Collaborative Filtering for Item Recommendation 41

Learning-Efficient Yet Generalizable Collaborative Filtering...

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41st International Conference on Machine Learning, ICML 2024

作者： Pu, Yuanhao Chen, Xiaolong Huang, Xu Chen, Jin Lian, Defu Chen, Enhong School of Artificial Intelligence and Data Science University of Science and Technology of China China School of Computer Science and Technology University of Science and Technology of China China School of Business and Management Hong Kong University of Science and Technology Hong Kong State Key Laboratory of Cognitive Intelligence China

The weighted squared loss is a common component in several Collaborative Filtering (CF) algorithms for item recommendation, including the representative implicit Alternating Least Squares (iALS). Despite its widespread use, this loss function lacks a clear connection to ranking objectives such as Discounted Cumulative Gain (DCG), posing a fundamental challenge in explaining the exceptional ranking performance observed in these algorithms. In this work, we make a breakthrough by establishing a connection between squared loss and ranking metrics through a Taylor expansion of the DCG-consistent surrogate loss-softmax loss. We also discover a new surrogate squared loss function, namely Ranking-Generalizable Squared (RG2) loss, and conduct thorough theoretical analyses on the DCG-consistency of the proposed loss function. Later, we present an example of utilizing the RG2 loss with Matrix Factorization (MF), coupled with a generalization upper bound and an ALS optimization algorithm that leverages closed-form solutions over all items. Experimental results over three public datasets demonstrate the effectiveness of the RG2 loss, exhibiting ranking performance on par with, or even surpassing, the softmax loss while achieving faster convergence. Copyright 2024 by the author(s)

关键词： Collaborative filtering

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