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arXiv 2024年

作者： Zhang, Cong Du, Shuyi Song, Hongqing Wang, Yuhe National & Local Joint Engineering Lab for Big Data Analysis and Computing Technology Beijing100190 China School of Civil and Resource Engineering University of Science and Technology Beijing Beijing100083 China Institute for Scientific Computation Texas A&M University College StationTX77843 United States

Estimating spatially distributed information through the interpolation of scattered observation datasets often overlooks the critical role of domain knowledge in understanding spatial dependencies. Additionally, the features of these datasets are typically limited to the spatial coordinates of the scattered observation locations. In this paper, we propose a hybrid framework that integrates data-driven spatial dependency feature extraction with rule-assisted spatial dependency function mapping to augment domain knowledge. We demonstrate the superior performance of our framework in two comparative application scenarios, highlighting its ability to capture more localized spatial features in the reconstructed distribution fields. Furthermore, we underscore its potential to enhance nonlinear estimation capabilities through the application of transformed fuzzy rules and to quantify the inherent uncertainties associated with the observation datasets. Our framework introduces an innovative approach to spatial information estimation by synergistically combining observational data with rule-assisted domain knowledge. © 2024, CC BY.

关键词： Domain Knowledge

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HairDiffusion: Vivid Multi-Colored Hair Editing via Latent Diffusion

arXiv

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arXiv 2024年

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, CC BY.

关键词： Color image processing

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A Novel Effectiveness Assessment Framework for Neoadjuvant Chemoradiotherapy of Locally Advanced Rectal Cancer Based on Multi-modal Intelligence

A Novel Effectiveness Assessment Framework for Neoadjuvant C...

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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Xiao Tian Dong Sui Weifeng Liu Maozu Guo Gongning Luo Kuanquan Wang Beijing Key Laboratory for Intelligent Processing Methods of Architectural Big Data Beijing China School of Electrical and Information Engineering Beijing University of Civil Engineering and Architecture Beijing China Perceptual Computing Research Center Harbin Institute of Technology Harbin China

ISBN: (数字)9798350337488

ISBN: (纸本)9798350337495

Neoadjuvant chemoradiotherapy (nCRT) is the stan-dard treatment for locally advanced rectal cancer (LARC). With the development of artificial intelligence, an increasing number of studies have begun to explore its application in cancer treatment prediction. However, the prior methods exhibit considerable variability even with slight modifications to the input data, which could potentially undermine the reliability of the results. In this paper, we proposed RP-Net, a novel multi-modal fusion-based framework that combines feature information from magnetic resonance imaging (MRI) and whole slide images (WSI), establishing a relationship to map the therapeutic effectiveness of nCRT for LARC. We investigated the relationship of the tumour region and its periphery tissues, and demonstrated the validity of the proposed framework that involving 11 different combinations of modalities. The experimental results revealed that it has achieved higher prediction accuracy compared to the four intra-categories single-modal combinations and outperformed the two intra-categories multi-modal combinations. When compared to the other four inter-categories multi-modal combinations, the fusion features get accuracy of 2 % ~ 6% improvement respectively.

关键词： Protocols Magnetic resonance imaging Decision making Cancer treatment Reliability Bioinformatics Artificial intelligence

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Reliable Indoor Localization in Multi-Building Environments: Leveraging Environment-Invariant and Position-Related Features

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IEEE Internet of Things Journal 2025年

作者： Long, Wenhan Wen, Xinlong Liu, Hao Li, Mengyao Li, Songquan Wu, Yonghui Chen, Fuxiang Liu, Lu Zhu, Rongbo Huazhong Agricultural University College of Informatics Wuhan430070 China Ministry of Education Engineering Research Center of Intelligent Technology for Agriculture Wuhan430070 China Hubei Engineering Technology Research Center of Agricultural Big Data Wuhan430070 China University of Leicester School of Computing and Mathematical Sciences Leicester United Kingdom University of Exeter Department of Computer Science Exeter United Kingdom

Received Signal Strength Indicator (RSSI)-based indoor localization offers a cost-effective solution for autonomous mobile robot navigation in 3D indoor environments, including cross-floor and multi-building structures. However, localization accuracy is fundamentally constrained by the low sampling density and unstable measurement of RSSI data. So far, existing methods neglect cross-environment RSSI coherence (e.g., repeated signal patterns in geometrically similar areas), resulting in unreliable fingerprint databases. What’s more, most approaches fail to model the spatial hierarchy of buildings, floors, and coordinates, which leads to lower accuracy in indoor positioning model predictions. To address these issues, we propose EP-3DLoc, a novel 3D indoor localization framework that combines an Environment-Invariant feature-based data Completion (EIC) method with a Position-Related feature-based Localization (PRL) method. The EIC enhances data quality by filling in sparse RSSI data using environment-invariant features, which are recurring RSSI patterns found in similar environmental structures. The PRL module combines multi-scale RSSI signal processing (raw data and image-like data) with a multi-task network that analyzes location relationships, enhancing localization accuracy in 3D environments. Experimental results on public datasets (TUT2018, UTSIndoorLoc, and UJIIndoorLoc) have demonstrated that EP-3DLoc achieves state-of-the-art performance on indoor localization in multi-building environments. Further testing on the self-constructed dataset HZAUIndoorLoc have revealed that EP-3DLoc not only outperforms existing methods in localization accuracy but also maintains low energy consumption and strong resistance to interference. © 2014 IEEE.

关键词： Floors

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From General to Specific: Tailoring Large Language Models for Personalized Healthcare

arXiv

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arXiv 2024年

作者： Shi, Ruize Huang, Hong Zhou, Wei Yin, Kehan Zhao, Kai Zhao, Yun Huazhong University of Science and Technology Wuhan China Tongji Medical College China Hubei Maternity and Child Health Care Hospital China National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Cluster and Grid Computing Lab School of Computer Science and Technology Huazhong University of Science and Technology China

The rapid development of large language models (LLMs) has transformed many industries, including healthcare. However, previous medical LLMs have largely focused on leveraging general medical knowledge to provide responses, without accounting for patient variability and lacking true personalization at the individual level. To address this, we propose a novel method called personalized medical language model (PMLM), which explores and optimizes personalized LLMs through recommendation systems and reinforcement learning (RL). Specifically, by utilizing self-informed and peer-informed personalization, PMLM captures changes in behaviors and preferences to design initial personalized prompts tailored to individual needs. We further refine these initial personalized prompts through RL, ultimately enhancing the precision of LLM guidance. Notably, the personalized prompt are hard prompt, which grants PMLM high adaptability and reusability, allowing it to directly leverage high-quality proprietary LLMs. We evaluate PMLM using real-world obstetrics and gynecology data, and the experimental results demonstrate that PMLM achieves personalized responses, and it provides more refined and individualized services, offering a potential way for personalized medical LLMs. Copyright © 2024, The Authors. All rights reserved.

关键词： Reusability

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Target Networks and Over-parameterization Stabilize Off-policy Bootstrapping with Function Approximation

arXiv

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arXiv 2024年

作者： Che, Fengdi Xiao, Chenjun Mei, Jincheng Dai, Bo Gummadi, Ramki Ramirez, Oscar A. Harris, Christopher K. Mahmood, A. Rupam Schuurmans, Dale Department of Computing Science University of Alberta United States School of Data Science The Chinese University of Hong Kong Shenzhen China Google DeepMind United Kingdom School of Computational Science and Engineering Georgia Tech United States Figure Uber United States CIFAR AI Chair Amii Canada

We prove that the combination of a target network and over-parameterized linear function approximation establishes a weaker convergence condition for bootstrapped value estimation in certain cases, even with off-policy data. Our condition is naturally satisfied for expected updates over the entire state-action space or learning with a batch of complete trajectories from episodic Markov decision processes. Notably, using only a target network or an over-parameterized model does not provide such a convergence guarantee. Additionally, we extend our results to learning with truncated trajectories, showing that convergence is achievable for all tasks with minor modifications, akin to value truncation for the final states in trajectories. Our primary result focuses on temporal difference estimation for prediction, providing high-probability value estimation error bounds and empirical analysis on Baird’s counterexample and a Four-room task. 1 Furthermore, we explore the control setting, demonstrating that similar convergence conditions apply to Q-learning. © 2024, CC BY-NC-ND.

关键词： Markov processes

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Multi-Modal data Fusion Approach Using the Structured and Sparse Principal Component Analysis (MMSSCA)

Multi-Modal Data Fusion Approach Using the Structured and Sp...

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Paradigm Shift in Information Technologies with Innovative Applications in Global Scenario (ICPSITIAGS), IEEE International Conference on

作者： Jyoti Kandpal A Jonitha Janaki Sivakumar Rejon Kumar Ray School of Computing Graphic Era Hill University Dehradun Uttarakhand India Faculty of Data Science & Computing Universiti Malaysia Kelantan Malaysia Computer Science and Creative Technologies Global College of Engineering and Technology Muscat Oman Department of Business analytics Gannon University Erie PA USA

ISBN: (数字)9798350329773

ISBN: (纸本)9798350329780

There are many different sorts of data that can be gathered and analyzed, including pictures, videos, texts, speeches, music, and other noises, Video content, for example, generally includes minimum some types of audible sounds or any type of visual image, and often also useful text data that can be of some specific type of titles or subtitles, which may consist of some unique type text language which is different type of speech. “Modality” is basically defines each different type of acquisition strategy. As natural phenomena tend to be quite complex, it is unusual for a single modality to provide exhaustive understanding of the phenomenon of interest. The process of combining and interpreting the useful data between the same modal or in different modal, and also interaction with the human beings with multi-modal data fusion &the accurate and deep interpretation and understanding of data obtained from the combining process from it, are central to the research challenge on multimodal data analysis. We contend that many of these challenges are shared by researchers in different fields. Both the “why” and the “how” of data fusion are addressed in this paper. Various examples from the realms of science and technology serve as inspiration for the first problem, which is then suggested by a various types of mathematical based model framework demonstrating the utility of data fusion. To solve the second problem, we introduce the idea of “diversity” as a central concept and discuss several data-driven solutions based on matrix and tensor decompositions, highlighting how they take into account diversity in the underlying data sets to address the aforementioned issues, this research suggests a novel approach for modeling multi data fusion implemented using sparse technique and structural analysis.

关键词： Visualization Tensors Diversity reception Noise data integration Music data models

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Has My Release Disobeyed Semantic Versioning? Static Detection Based on Semantic Differencing 22

Has My Release Disobeyed Semantic Versioning? Static Detecti...

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Proceedings of the 37th IEEE/ACM International Conference on Automated Software engineering

作者： Lyuye Zhang Chengwei Liu Zhengzi Xu Sen Chen Lingling Fan Bihuan Chen Yang Liu School of Computer Science and Engineering Nanyang Technological University Singapore College of Intelligence and Computing Tianjin University China College of Cyber Science Nankai University China School of Computer Science and Shanghai Key Laboratory of Data Science Fudan University China

ISBN: (纸本)9781450394758

To enhance the compatibility in the version control of Java Third-party Libraries (TPLs), Maven adopts Semantic Versioning (SemVer) to standardize the underlying meaning of versions, but users could still confront abnormal execution and crash after upgrades even if compilation and linkage succeed. It is caused by semantic breaking (SemB) issues, such that APIs directly used by users have identical signatures but inconsistent semantics across upgrades. To strengthen compliance with SemVer rules, developers and users should be alerted of such issues. Unfortunately, it is challenging to detect them statically, because semantic changes in the internal methods of APIs are difficult to capture. Dynamic testing can confirmingly uncover some, but it is limited by inadequate coverage. To detect SemB issues over compatible upgrades (Patch and Minor) by SemVer rules, we conduct an empirical study on 180 SemB issues to understand the root causes, inspired by which, we propose Sembid (Semantic Breaking Issue Detector) to statically detect such issues of TPLs for developers and users. Since APIs are directly used by users, Sembid detects and reports SemB issues based on APIs. For a pair of APIs, Sembid walks through the call chains originating from the API to locate breaking changes by measuring semantic diff. Then, Sembid checks if the breaking changes can affect API’s output along call chains. The evaluation showed Sembid achieved recall and precision and outperformed other API checkers on SemB API detection. We also revealed Sembid detected over 3 times more SemB APIs with better coverage than unit tests, the commonly used solution. Furthermore, we carried out an empirical study on 1,629,589 APIs from 546 version pairs of top Java libraries and found there were 2 ∼ 4 times more SemB APIs than those with signature-based issues. Due to various version release strategies, of Patch version pairs and of Minor version pairs had at least one API affected by any breaking.

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Collaborative Edge Service Placement for Maximizing QoS with Distributed data Cleaning 31

Collaborative Edge Service Placement for Maximizing QoS with...

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31st IEEE/ACM International Symposium on Quality of Service, IWQoS 2023

作者： Liang, Yuzhu Wang, Wenhua Zheng, Xi Liu, Qin Wang, Liang Wang, Tian School of Artificial Intelligence Beijing Normal University Beijing100875 China School of Computing Macquarie University NSW2109 Australia College of Computer Science and Electronic Engineering Hunan University Changsha410082 China School of Computer Science Northwestern Polytechnical University Xi'an710072 China BNU-UIC Institute of Artificial Intelligence and Future Networks Beijing Normal University Zhuhai519000 China BNU-HKBU United International College Guangdong Key Lab of Ai and Multi-Modal Data Processing Zhuhai519000 China

ISBN: (纸本)9798350399738

The proliferation of dirty data on Internet of Things (IoT) devices can undermine the accuracy of data-driven decision-making by affecting the distribution of original data. The Quality of Service (QoS) of data cleaning on these devices is heavily impacted by processing delay and accuracy. In this paper, we find that edge service placement is a key step aligned with data cleaning and consider the collaborative edge service placement with distributed data cleaning (SPDC) problem. To address this issue, we propose a novel distributed collaborative edge-based architecture that effectively balances the demands of storage, communication, computation, and load constraints. Experimental results show that the proposed approach significantly improves the accuracy of data cleaning by 0.31%-86.07% and reduces delay by 2.73%-58.71% compared to state-of-the-art baselines. © 2023 IEEE.

关键词： Edge computing

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Synopsis: Privacy Meets Performance: Enhancing Distributed Simulation-based Federated Multi-agent Learning with Privacy-preserving Surrogate Model✱ 25

Synopsis: Privacy Meets Performance: Enhancing Distributed S...

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39th ACM SIGSIM Conference on Principles of Advanced Discrete Simulation

作者： Bo Zhang Wen Jun Tan Wentong Cai Allan Zhang College of Computing and Data Science Nanyang Technological University Singapore Singapore School of Computer Science and Engineering Nanyang Technological University Singapore Singapore Nanyang Technological University Singapore Singapore Nanyang Technological University Singapore Singapore and Singapore Institute of Manufacturing Technology Singapore Singapore

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