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40th IEEE International Conference on data engineering, ICDE 2024

作者： Ding, Xiaoou Liu, Yida Wang, Hongzhi Wang, Chen Song, Yichen Yang, Donghua Wang, Jianmin School of Computer Science and Technology Harbin Institute of Technology China National Engineering Research Center for Big Data Software EIRI Tsinghua University China

ISBN: (纸本)9798350317152

Assessing data quality through Functional Depen-dencies (FDs) is a crucial aspect of data governance. However, with the diverse range of data sources and the exponential growth in data volume, exact FDs can sometimes be impractical for real-world applications. In contrast, relaxed functional dependencies (RFDs), which allows for some flexibility in attribute value comparisons, demonstrates greater adaptability and flexibility for big data scenarios. To address the efficient discovery of RFDs, this paper proposes a novel mining method to supplement the current research gaps. By establishing a difference table for tuples, we transform the problem into a specialized minimal set covering problem. Additionally, we introduce two optimization strategies: reducing the time complexity of enumerating the left-hand side of the base RFDs to 0 (1) and decreasing the search complexity for feasible LHS attributes and threshold candidates from O(2m-l) to O(1.5m-1). We rigorously proof that our mining approach guarantees the identification of validity and minimal RFDs. Experiments on nine real-world datasets reveal that our method significantly improves efficiency compared to existing techniques. Furthermore, it uncovers more concise and higher-quality RFDs. Importantly, the RFDs extracted through our methodology exhibit better performance in downstream cleaning tasks. © 2024 IEEE.

关键词： Quality management

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TSDDISCOVER: Discovering data Dependency for Time Series data 40

TSDDISCOVER: Discovering Data Dependency for Time Series Dat...

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40th IEEE International Conference on data engineering, ICDE 2024

作者： Ding, Xiaoou Li, Yingze Wang, Hongzhi Wang, Chen Liu, Yida Wang, Jianmin School of Computer Science and Technology Harbin Institute of Technology China Tsinghua University National Engineering Research Center for Big Data Software EIRI China

ISBN: (纸本)9798350317152

Intelligent devices often produce time series data that suffer from significant data quality issues. While the utilization of data dependency in error detection and data repair has been somewhat beneficial, it remains inadequate in accurately representing the data quality of time series datasets. In recognition of the obvious characteristics inherent in time series data, we introduce a novel data dependency, termed TSDD. It effectively captures the contextual relationships embedded within multivariate time series, thereby enhancing the semantic richness of data quality representations. We analyze the complexity of both implication and consistency problems for TSDD reasoning, and develop TSDD discovery algorithm TSDDISCOVER, which consists of functional structure discovery, allowable error bound determination, and validation of TSDD patterns. Experimental results on real-life datasets verify TSDDISCOVER efficiently discovers high-quality TSDD patterns. In comparing the performance of TSDD-based error detection with several leading data quality constraints, our findings reveal that the former achieves an average improvement of 12% in accuracy and 30% in the F1 score over other dependency-based detection methods. © 2024 IEEE.

关键词： Semantics

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Effectiveness of machine learning at modeling the relationship between Hi-C data and copy number variation

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Quantitative Biology 2024年第3期12卷 231-244页

作者： Yuyang Wang Yu Sun Zeyu Liu Bijia Chen Hebing Chen Chao Ren Xuanwei Lin Pengzhen Hu Peiheng Jia Xiang Xu Kang Xu Ximeng Liu Hao Li Xiaochen Bo Institute of Health Service and Transfusion Medicine BeijingChina College of Computer and Data Science Fuzhou UniversityFuzhouChina Beijing Institute of Radiation Medicine BeijingChina School of Life Sciences Northwestern Polytechnical UniversityXi’anChina School of Mathematics and Computer Science Shanxi Normal UniversityTaiyuanChina School of Software Shandong UniversityQingdaoChina

Copy number variation(CNV)refers to the number of copies of a specific sequence in a genome and is a type of chromatin structural *** development of the Hi-C technique has empowered research on the spatial structure of chromatins by capturing interactions between DNA *** utilized machine-learning methods including the linear transformation model and graph convolutional network(GCN)to detect CNV events from Hi-C data and reveal how CNV is related to three-dimensional interactions between genomic fragments in terms of the one-dimensional read count signal and features of the chromatin *** experimental results demonstrated a specific linear relation between the Hi-C read count and CNV for each chromosome that can be well qualified by the linear transformation *** addition,the GCN-based model could accurately extract features of the spatial structure from Hi-C data and infer the corresponding CNV across different chromosomes in a cancer cell *** performed a series of experiments including dimension reduction,transfer learning,and Hi-C data perturbation to comprehensively evaluate the utility and robustness of the GCN-based *** work can provide a benchmark for using machine learning to infer CNV from Hi-C data and serves as a necessary foundation for deeper understanding of the relationship between Hi-C data and CNV.

关键词： copy number variant deep learning graph convolution network Hi-C

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Calibration Signal Assisted Emitter Localization under Sensor Position Uncertainty

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IEEE Transactions on Aerospace and Electronic Systems 2025年第3期61卷 6913-6927页

作者： You, Ming-Yi Pu, Wenqiang Zhou, Rui Ye, Yun-Xia Xiao, Minrui Wang, Wei Shi, Qingjiang No.36 Research Institute of CETC National Key Laboratory of Electromagnetic Space Security Jiaxing314033 China The Chinese University of Hong Kong-Shenzhen Shenzhen Research Institute of Big Data Shenzhen518172 China Shanghai Jiaotong University School of Sensing Science and Engineering Shanghai200240 China Tongji University School of Software Engineering Shanghai200070 China

The uncertainty of sensor position significantly compromises the accuracy of source localization within the direct position determination (DPD) framework. This paper explores the use of an external calibrator that emits a calibration signal with a known position to mitigate the impact of sensor position uncertainty on localization accuracy. By utilizing the calibration signal, we develop a refined Gibbs sampling algorithm as the initial step to effectively address the high-dimensional optimization problem associated with sensor position calibration. In the second step, we account for measurement noise and estimation uncertainties in the calibrated sensor positions and propose a revised DPD method, which demonstrates greater resilience to sensor position uncertainty compared to existing methods. We also derive the Cramer-Rao Lower Bound for emitter position under scenarios both with and without an external calibrator. Numerical simulations are performed to evaluate the performance of our proposed algorithms against several benchmark methods, demonstrating the effectiveness of our approach. © 1965-2011 IEEE.

关键词： Cramer-Rao bounds

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CBENet: contextual and boundary-enhanced network for oil spill detection via microwave remote sensing

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Intelligent Marine Technology and Systems 2025年第1期3卷 1-10页

作者： Di, Mengmeng Di, Xinnan Xiao, Huiyao Gao, Ying Li, Yongqing China Research Institute of Radiowave Propagation Qingdao China School of Data Science Qingdao University of Science and Technology Qingdao China School of Information and Control Engineering Qingdao University of Technology Qingdao China

In microwave remote sensing images, oil spills are generally distributed at various scales with blurry boundaries. To accurately detect variable oil spills from microwave remote sensing images, especially from synthetic aperture radar (SAR) images, we developed a contextual and boundary-enhanced network (CBENet) for oil spill detection from SAR observation images. The CBENet employs an encoder-decoder architecture that includes an encoder, a contextual fusion module, and a decoder. The encoder-decoder architecture intrinsically captures both global and local features of oil spills through the downsampling and upsampling processes inherent to the framework. The contextual fusion module enhances the contextual feature fusion using parallel dilated convolution branches. Furthermore, the utilization of a boundary-enhanced loss function further improves detection by focusing on the precise identification of oil spill boundaries. These properties strengthen the CBENet to effectively detect oil spills with blurry boundaries. The effectiveness of the CBENet has been validated through comprehensive empirical experiments including qualitative and quantitative evaluations. Comparative analyses demonstrate that CBENet outperforms several state-of-the-art detection models, significantly enhancing oil spill detection accuracy from SAR observation images.

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Regulating band structure, charge transfer and separation, oxygen adsorption and activation by surface ion modification

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Environmental science and Pollution Research 2024年第36期31卷 49172-49184页

作者： Zhang, Hanming Liu, Zhe Teng, Yiran Mei, Zhisheng Jiang, Tao Dong, Bingzheng Yang, Yalin Xu, Tongguang Teng, Fei School of Environmental Science and Engineering Nanjing University of Information Science and Technology 219 Ningliu Road Nanjing210044 China School of Environmental Science and Engineering Jilin Normal University 1306 Haifeng Street Siping136000 China Nanjing Software Research Institute of China United Network Communications Co. Ltd 230 Lushan Road Nanjing210004 China School of Science and Information Science Qingdao Agricultural University Qingdao266109 China Beijing Third Class Tobacco Supervision Station Beijing101121 China

As a most promising environmental technology, the substantial enhancement of photocatalytic efficiency is still a big challenge for practical applications. In this work, the surface of Bi2O2CO3 (BOC) nanotubes are modified by Cl and I. The as-obtained samples at different hydrothermal temperatures (T) are designated as T-X-BOC (X = Cl, I). X-ray diffraction (XRD), energy dispersive X-ray (EDX) spectroscopy and X-ray photoelectron spectroscopy (XPS) prove that Cl and I merely chemically adsorb on the BOC surface, rather than dope into the crystal lattice. The surface modification of Cl and I slightly increases light absorption range, while significantly promotes the photoelectron migration from bulk to the surface that greatly enhances the carrier separation efficiency. Density functional theory (DFT) calculations further prove that surface Cl and I have adjusted band structure and surface charge distribution. Besides, the surface Cl and I favor the O2 adsorption and trap the surface photoelectrons, thus promoting the formation of •O2–;while the surface Cl and I impede the surface adsorption of H2O, thus refraining the generation of •OH. In the degradation of rhodamine B (RhB), holes and •O2− radicals play the crucial role. Under ultraviolet light irradiation (λ © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2024.

关键词： Photoelectrons

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MDE and Learning for flexible Planning and optimized Execution of Multi-Robot Choreographies 28

MDE and Learning for flexible Planning and optimized Executi...

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28th IEEE International Conference on Emerging Technologies and Factory Automation, ETFA 2023

作者： Wete, Eric Greenyer, Joel Wortmann, Andreas Kudenko, Daniel Nejdl, Wolfgang Leibniz University of Hanover Institute of Data Science Hanover Germany FHDW Hanover Institute for Software Engineering Hanover Germany University of Stuttgart ISW Stuttgart Germany

ISBN: (纸本)9798350339918

Multi-Robot systems in automotive are safety-critical systems that consist of collaborating-aware robots and components that interact with external components, the environment, or humans at run-time. This implies a significant complexity for the system engineer to design, model, validate the system, and optimize the cycle time, including considering unexpected events at run-time. This paper addresses this challenge by describing a model-driven engineering approach that formally designs the system under the consideration of uncertainties and at run-time optimizes the system actions using learning-based approaches. We implemented this approach in an industrial-inspired case study of a spot-welding multi-robot cell. Based on the system requirements, we generate valid system strategies that consider unexpected events such as robot interruptions and failures. Considering movement and interruption time models, we implemented a reinforcement learning method to optimize system actions at run-time. We show that via simulations and learning, our approach can be used to synthesize time-efficient schedules for robot task assignments that improve the overall cycle time. © 2023 IEEE.

关键词： Motion planning

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Semantic consistency assisted change detection in remote sensing images

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National Remote Sensing Bulletin 2025年第4期29卷 1031-1045页

作者： Wu, Hucheng Wang, Renfang Qiu, Hong Wang, Feng Gao, Guang Wu, Dun College of Computer Science and Technology Ocean University of China Qingdao266100 China College of Big Data and Software Engineering Zhejiang Wanli University Ningbo315100 China Co. Ltd. Ningbo315042 China

Semantic change detection of remote sensing images plays an important role in ecological environment, land use, and land cover monitoring. In recent years, deep learning-based change detection methods are the hotspot of remote sensing intelligent interpretation concern;however, the existing three-branch semantic change detection methods lack consistency of change branch and semantic branches, which leads to the self-contradiction of the bitemporal semantic change detection. To address this problem, this study proposes a remote sensing image semantic change detection algorithm based on Siamese CNN and Transformer. In the encoding stage, the Siamese ResNet34 network is first designed to extract the multiscale features of the image, and the difference enhancement module is embedded to increase the attention of the change information;then, the semantic tokenizer is used to map the feature map into compact semantic tokens, and the Transformer encoder is used to combine the bitemporal semantics and the change information to model the"semantic change"consistency. In the decoding stage, different fine-grained semantic information is fused by the Transformer decoder using a hopping connection to generate a refined semantic feature map. Finally, the result of bitemporal semantic change is obtained after upsampling recovery and mask multiplication. Experimental results on the remote sensing semantic change detection public dataset SECOND and Landsat-SCD show that the proposed algorithm can effectively focus on the change region, maintain the consistency between the change results and the semantic results, and achieve excellent evaluation indexes and visual effects. We can draw the following conclusions: (1) The proposed difference enhancement module can enhance the difference characteristics of bitemporal remote sensing images and improve the network’s focus on change information. (2) The proposed bitemporal Transformer module maps the difference information and bitemporal sem

关键词： Change detection

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Imitation Learning from Purified Demonstrations 41

Imitation Learning from Purified Demonstrations

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41st International Conference on Machine Learning, ICML 2024

作者： Wang, Yunke Dong, Minjing Zhao, Yukun Du, Bo Xu, Chang School of Computer Science National Engineering Research Center for Multimedia Software Institute of Artificial Intelligence Wuhan Institute of Data Intelligence Wuhan University China Department of Computer Science City University of Hong Kong Hong Kong 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. Copyright 2024 by the author(s)

关键词： Contrastive Learning

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Locating What You Need: Towards Adapting Diffusion Models to OOD Concepts In-the-Wild 38

Locating What You Need: Towards Adapting Diffusion Models to...

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38th Conference on Neural Information Processing Systems, NeurIPS 2024

作者： Yang, Jianan Gao, Chenchao Xiao, Zhiqing Zhao, Junbo Wu, Sai Chen, Gang Wang, Haobo College of Computer Science and Technology Zhejiang University China Institute of Blockchain and Data Security China School of Software Technology Zhejiang University China International School of Information Science and Engineering Dalian University of Technology China

The recent large-scale text-to-image generative models have attained unprecedented performance, while people established adaptor modules like LoRA and DreamBooth to extend this performance to even more unseen concept tokens. However, we empirically find that this workflow often fails to accurately depict the out-of-distribution concepts. This failure is highly related to the low quality of training data. To resolve this, we present a framework called Controllable Adaptor Towards Out-of-Distribution Concepts (CATOD). Our framework follows the active learning paradigm which includes high-quality data accumulation and adaptor training, enabling a finer-grained enhancement of generative results. The aesthetics score and concept-matching score are two major factors that impact the quality of synthetic results. One key component of CATOD is the weighted scoring system that automatically balances between these two scores and we also offer comprehensive theoretical analysis for this point. Then, it determines how to select data and schedule the adaptor training based on this scoring system. The extensive results show that CATOD significantly outperforms the prior approaches with an 11.10 boost on the CLIP score and a 33.08% decrease on the CMMD metric. © 2024 Neural information processing systems foundation. All rights reserved.

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