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38th Conference on Neural Information Processing Systems, NeurIPS 2024

作者： Zeng, Yu Zhang, Yang Liu, Jiachen Shen, Linlin Deng, Kaijun He, Weizhao Wang, Jinbao Computer Vision Institute School of Computer Science & Software Engineering Shenzhen University China Shenzhen Institute of Artificial Intelligence and Robotics for Society China National Engineering Laboratory for Big Data System Computing Technology Shenzhen University China Guangdong Provincial Key Laboratory of Intelligent Information Processing China

Hair editing is a critical image synthesis task that aims to edit hair color and hairstyle using text descriptions or reference images, while preserving irrelevant attributes (e.g., identity, background, cloth). Many existing methods are based on StyleGAN to address this task. However, due to the limited spatial distribution of StyleGAN, it struggles with multiple hair color editing and facial preservation. Considering the advancements in diffusion models, we utilize Latent Diffusion Models (LDMs) for hairstyle editing. Our approach introduces Multi-stage Hairstyle Blend (MHB), effectively separating control of hair color and hairstyle in diffusion latent space. Additionally, we train a warping module to align the hair color with the target region. To further enhance multi-color hairstyle editing, we fine-tuned a CLIP model using a multi-color hairstyle dataset. Our method not only tackles the complexity of multi-color hairstyles but also addresses the challenge of preserving original colors during diffusion editing. Extensive experiments showcase the superiority of our method in editing multi-color hairstyles while preserving facial attributes given textual descriptions and reference images. © 2024 Neural information processing systems foundation. All rights reserved.

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data Aggregation Point Placement and Subnetwork Optimization for Smart Grids

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Computers, Materials & Continua 2025年第4期83卷 407-434页

作者： Tien-Wen Sung Wei Li Chao-Yang Lee Yuzhen Chen Qingjun Fang Fujian Provincial Key Laboratory of Big Data Mining and Applications College of Computer Science and MathematicsFujian University of TechnologyFuzhou350118China Department of Computer Science and Information Engineering National Yunlin University of Science and TechnologyYunlin640301TaiwanChina

To transmit customer power data collected by smart meters(SMs)to utility companies,data must first be transmitted to the corresponding data aggregation point(DAP)of the *** number of DAPs installed and the installation location greatly impact the whole *** the traditional DAP placement algorithm,the number of DAPs must be set in advance,but determining the best number of DAPs is difficult,which undoubtedly reduces the overall performance of the ***,the excessive gap between the loads of different DAPs is also an important factor affecting the quality of the *** address the above problems,this paper proposes a DAP placement algorithm,APSSA,based on the improved affinity propagation(AP)algorithm and sparrow search(SSA)algorithm,which can select the appropriate number of DAPs to be installed and the corresponding installation locations according to the number of SMs and their distribution locations in different *** algorithm adds an allocation mechanism to optimize the subnetwork in the *** is evaluated under three different areas and compared with other DAP placement *** experimental results validated that the method in this paper can reduce the network cost,shorten the average transmission distance,and reduce the load gap.

关键词： Smart grid data aggregation point placement network cost average transmission distance load gap

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A Polynomial-Time Algorithm for MCS Partial Search Order on Chordal Graphs 48

A Polynomial-Time Algorithm for MCS Partial Search Order on ...

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48th International Symposium on Mathematical Foundations of Computer Science, MFCS 2023

作者： Rong, Guozhen Yang, Yongjie Li, Wenjun Hunan Provincial Key Laboratory of Intelligent Processing of Big Data on Transportation Changsha University of Science and Technology China Chair of Economic Theory Saarland University Saarbrücken Germany

ISBN: (纸本)9783959772921

We study the partial search order problem (PSOP) proposed recently by Scheffler [WG 2022]. Given a graph G together with a partial order on the set of vertices of G, this problem determines if there is an S-ordering that is consistent with the given partial order, where S is a graph search paradigm like BFS, DFS, etc. This problem naturally generalizes the end-vertex problem which has received much attention over the past few years. It also generalizes the so-called F-tree recognition problem which has just been studied in the literature recently. Our main contribution is a polynomial-time dynamic programming algorithm for the PSOP of the maximum cardinality search (MCS) restricted to chordal graphs. This resolves one of the most intriguing open questions left in the work of Scheffler [WG 2022]. To obtain our result, we propose the notion of layer structure and study numerous related structural properties which might be of independent interest. © Guozhen Rong, Yongjie Yang, and Wenjun Li

关键词： Dynamic programming

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Influencing Factors of Healthy Aging Risk Assessed Using Biomarkers:A Life Course Perspective

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China CDC weekly 2024年第11期6卷 219-224,I0014-I0017页

作者： Cedric Zhang Bo Lua Yajie Gao Jinming Li Xingqi Cao Xinwei Lyu Yinuo Tu Shuyi Jin Zuyun Liu Center for Clinical Big Data and Analytics of the Second Affiliated Hospital and Department of Big Data in Health Science School of Public Healththe Key Laboratory of Intelligent Preventive Medicine of Zhejiang ProvinceZhejiang University School of MedicineHangzhou CityZhejiang ProvinceChina Institute of Epidemiology and Health Care University College LondonLondonUK College of Chemical and Biological Engineering Zhejiang UniversityHangzhou CityZhejiang ProvinceChina

Assessing individual risks of healthy aging using biomarkers and identifying associated factors have become important areas of *** this study,we conducted a literature review of relevant publications between 2018 and 2023 in both Chinese and English *** studies have predominantly used single biomarkers,such as C-reactive protein,or focused on specific life course stages and factors such as socioeconomic status,mental health,educational levels,and unhealthy *** summarizing the progress in this field,our study provides valuable insights and future directions for promoting healthy aging from a life course perspective.

关键词： such insight directions

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Improved Demonstration-Knowledge Utilization in Reinforcement Learning

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2024年第5期5卷 2139-2150页

作者： Liu, Yanyu Zeng, Yifeng Ma, Biyang Pan, Yinghui Gao, Huifan Zhang, Yuting Xiamen University Xiamen Key Laboratory of Big Data Intelligent Analysis and Decision Department of Automation Xiamen361102 China Northumbria University Department of Computer and Information Sciences NewcastleNE1 8ST United Kingdom Minnan Normal University Department of Computer Science Zhangzhou363000 China Shenzhen University National Engineering Laboratory for Big Data System Computing Technology Shenzhen518060 China

Reinforcement learning (RL) has made great success in recent years. Generally, the learning process requires a huge amount of interaction with the environment before an agent can achieve acceptable performance. This motivates many techniques, such as incorporating prior knowledge which is usually presented as experts' demonstration, and using a probability distribution to represent state-and-action values, to accelerate the learning process. The methods perform well when the prior knowledge is genuinely correct and no much change occurs to the learning environment. However, the requirement is not perfectly realistic in many complex applications. The demonstration knowledge may not reflect the true environment and even be full of noise. In this article, we introduce a dynamic distribution merging method to improve knowledge utilization in a general RL algorithm, namely Q-learning. The new method adapts a normal distribution to represent state-action values and merges the prior and learned knowledge in a discriminative way. We theoretically analyze the new learning method and demonstrate its empirical performance over multiple problem domains. © 2020 IEEE.

关键词： Merging

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A topic detection method based on KM-LSH Fusion algorithm and improved BTM model

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Soft Computing 2024年第19期28卷 11421-11438页

作者： Liu, Wenjun Guo, Huan Gan, Jiaxin Wang, Hai Wang, Hailan Zhang, Chao Peng, Qingcheng Sun, Yuyan Yu, Bao Hou, Mengshu Li, Bo Li, Xiaolei School of Computer and Software Engineering XiHua University Chengdu610039 China Chengdu611731 China Intelligent Policing Key Laboratory of Sichuan Province Sichuan Police College Luzhou646000 China School of Big Data and Artificial Intelligence Chengdu Technological University Chengdu611730 China Sichuan Provincial Engineering Research Center of Hydroelectric Energy Power Equipment Technology Chengdu610039 China

Topic detection is an information processing technology designed to help people deal with the growing problem of data information on the Internet. In the research literature, topic detection methods are used for topic classification through word embedding, supervised-based and unsupervised-based approaches. However, most methods for topic detection only address the problem of clustering and do not focus on the problem of topic detection accuracy reduction due to the cohesiveness of topics. Also, the sequence of biterm during topic detection can cause substantial deviations in the detected topic content. To solve the above problems, this paper proposes a topic detection method based on KM-LSH fusion algorithm and improved BTM model. KM-LSH fusion algorithm is a fusion algorithm that combines K-means clustering and LSH refinement clustering. The proposed method can solve the problem of cohesiveness of topic detection, and the improved BTM model can solve the influence of the sequence of biterm on topic detection. First, the text vector is constructed by processing the collected set of microblog texts using text preprocessing methods. Secondly, the KM-LSH fusion algorithm is used to calculate text similarity and perform topic clustering and refinement. Finally, the improved BTM model is used to model the texts, which is combined with the word position and the improved TF-IDF weight calculation algorithm to adjust the microblogging texts in clustering. The experiment results indicate that the proposed KM-LSH-IBTM method improves the evaluation indexes compared with the other three topic detection methods. In conclusion, the proposed KM-LSH-IBTM method promotes the processing capability of topic detection in terms of cohesiveness and the sequence of biterm. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2024.

关键词： K-means clustering

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Multi-modal Denoising Diffusion Pre-training for Whole-Slide Image Classification 24

Multi-modal Denoising Diffusion Pre-training for Whole-Slide...

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32nd ACM International Conference on Multimedia, MM 2024

作者： Lou, Wei Li, Guanbin Wan, Xiang Li, Haofeng The Chinese University of Hong Kong Shenzhen Shenzhen China Shenzhen Research Institute of Big Data Shenzhen China School of Computer Science and Engineering Sun Yat-sen University Guangzhou China Guangdong Provincial Key Laboratory of Big Data Computing Shenzhen China

ISBN: (纸本)9798400706868

Whole-slide image (WSI) classification methods play a crucial role in tumor diagnosis. Most of them use hematoxylin and eosin (H&E) stained images, while Immunohistochemistry (IHC) staining provides molecular markers and protein expression information that highlights cancer regions. However, obtaining IHC-stained images requires higher costs in practice. In this work, we propose a multi-modal denoising diffusion pre-training framework that harnesses the advantages of IHC staining to learn visual representations. The framework is trained with the H&E-to-IHC re-staining task and IHC-stained image reconstruction task, which helps capture the structural similarity and staining difference between two image modalities. The trained model can then provide IHC-guided features, by taking only H&E-stained images as inputs. Besides, we build a new class-constraint constrastive loss to achieve the semantic consistency between dual-modal features from our pre-training framework. To integrate with WSI classifiers based on multi-instance learning, we further propose a bag feature augmentation strategy to extend bags with the features extracted by our pre-trained model. Experimental results on three datasets show that our pre-training framework effectively improves WSI classification and surpasses the state-of-the-art pre-training approaches. Code and model are released via https://***/lhaof/MDDP. © 2024 ACM.

关键词： Image reconstruction

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Homomorphic Encryption based stem cell assay data sharing study 4

Homomorphic Encryption based stem cell assay data sharing st...

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4th IEEE International Conference on Information Technology, big data and Artificial Intelligence, ICIBA 2024

作者： Xiao, Hengjiang Liang, Zhihong Huang, Yuxiang Qin, Mingming Xiong, Fei Liu, Mengxin Southwest Forestry University School of Big Data and Intelligent Engineering Kunming China Southwest Forestry University Key Laboratory of National Forestry and Grassland Administration on Forestry and Ecological Big Data China Southwest Forestry University School of Economics and Management Kunming China

ISBN: (纸本)9798350363586

With the rapid development of the biomedical field, stem cell therapy has received widespread attention as an innovative therapy with great potential. However, privacy protection and secure sharing of stem cell testing data are still major challenges. In this paper, we propose a homomorphic encryption-based data sharing scheme for stem cell testing, which achieves efficient and secure data sharing with data privacy by designing a system architecture covering data acquisition, encryption, transmission, decryption and analysis. Experimental results show that the scheme can not only effectively protect data privacy, but also support efficient data processing with less impact on system performance. The main contributions of this paper include designing and implementing a stem cell detection data sharing system based on homomorphic encryption, proposing a homomorphic encryption algorithm optimised for the high-dimensional characteristics of stem cell data, and experimentally verifying the security and effectiveness of the system. Future research directions include optimising homomorphic encryption algorithms, expanding application scenarios, integrating multiple privacy protection techniques, and optimising system performance. © 2024 IEEE.

关键词： data assimilation

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Machine Learning for Signal Demodulation in Underwater Wireless Optical Communications

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China Communications 2024年第5期21卷 297-313页

作者： Ma Shuai Yang Lei Ding Wanying Li Hang Zhang Zhongdan Xu Jing Li Zongyan Xu Gang Li Shiyin Department of Strategic and Advanced Interdisciplinary Research Peng Cheng LaboratoryShenzhen 518055China School of Information and Control Engineering China University of Mining and TechnologyXuzhou 221116China Data-driven Intelligent Information System Laboratory Shenzhen Research Institute of Big DataShenzhen 518172China Ocean College Zhejiang UniversityZhoushan 316021China State Key Laboratory of Millimeter Waves Southeast UniversityNanjing 210096China

The underwater wireless optical communication(UWOC)system has gradually become essential to underwater wireless communication *** other existing works on UWOC systems,this paper evaluates the proposed machine learningbased signal demodulation methods through the selfbuilt experimental *** on such a platform,we first construct a real signal dataset with ten modulation ***,we propose a deep belief network(DBN)-based demodulator for feature extraction and multi-class feature *** also design an adaptive boosting(Ada Boost)demodulator as an alternative scheme without feature filtering for multiple modulated ***,it is demonstrated by extensive experimental results that the Ada Boost demodulator significantly outperforms the other *** also reveals that the demodulator accuracy decreases as the modulation order increases for a fixed received optical power.A higher-order modulation may achieve a higher effective transmission rate when the signal-to-noise ratio(SNR)is higher.

关键词： AdaBoost DBN machine learning signal demodulation

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key Clues Guided Video Character Social Relationship Recognition Enhanced by LLM

Key Clues Guided Video Character Social Relationship Recogni...

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2025 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2025

作者： Dong, Wenlong Zhu, Qing Mao, Qirong School of Computer Science and Communication Engineering Jiangsu University China Jiangsu Engineering Research Center of Big Data Ubiquitous Perception and Intelligent Agriculture Applications Provincial Key Laboratory of Computational Intelligence and New Technologies in Low-Altitude Digital Agriculture Zhenjiang China

ISBN: (纸本)9798350368741

Video Character Social Relationship Recognition (VCSRR) requires a comprehensive consideration about spatio-temporal and multi-modal clues in videos. Most existing methods mainly focus on integrating multi-modal clues and modeling interactions among characters. However, they fail to discover key clues in the complex video data or fully understand the clues related to social relationships. In this article, we propose a novel Large Language Model Enhanced key Clues Selection (LEKCS) framework to address the aforementioned issues. The core of LE-KCS is to mine multi-scale key clues from the perspectives of time, space and multi-modality, then transfer the knowledge about social relationships of the Large Language Model to VCSRR for understanding the selected clues. We evaluated LEKCS on the MovieGraphs dataset and the experimental results indicate that our proposed LE-KCS achieves state-of-the-art performance. © 2025 IEEE.

关键词： large language model Social relationship recognition video understanding

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