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38th Conference on Neural Information Processing Systems, NeurIPS 2024

作者： Meng, Benyuan Xu, Qianqian Wang, Zitai Cao, Xiaochun Huang, Qingming Institute of Information Engineering CAS China School of Cyber Security University of Chinese Academy of Sciences China Key Lab. of Intelligent Information Processing Institute of Computing Technology CAS China Peng Cheng Laboratory China School of Cyber Science and Tech. Shenzhen Campus of Sun Yat-sen University China School of Computer Science and Tech. University of Chinese Academy of Sciences China Key Laboratory of Big Data Mining and Knowledge Management CAS China

Diffusion models are initially designed for image generation. Recent research shows that the internal signals within their backbones, named activations, can also serve as dense features for various discriminative tasks such as semantic segmentation. Given numerous activations, selecting a small yet effective subset poses a fundamental problem. To this end, the early study of this field performs a large-scale quantitative comparison of the discriminative ability of the activations. However, we find that many potential activations have not been evaluated, such as the queries and keys used to compute attention scores. Moreover, recent advancements in diffusion architectures bring many new activations, such as those within embedded ViT modules. Both combined, activation selection remains unresolved but overlooked. To tackle this issue, this paper takes a further step with a much broader range of activations evaluated. Considering the significant increase in activations, a full-scale quantitative comparison is no longer operational. Instead, we seek to understand the properties of these activations, such that the activations that are clearly inferior can be filtered out in advance via simple qualitative evaluation. After careful analysis, we discover three properties universal among diffusion models, enabling this study to go beyond specific models. On top of this, we present effective feature selection solutions for several popular diffusion models. Finally, the experiments across multiple discriminative tasks validate the superiority of our method over the SOTA competitors. Our code is available at this url. © 2024 Neural information processing systems foundation. All rights reserved.

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SR-BigGAN: lightweight image super-resolution with priors

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Machine Vision and Applications 2025年第4期36卷 1-18页

作者： Kumar, Deepak Srinivas Rao, Harshitha Kumar, Chetan Shao, Ming Department of Computer Science Worcester Polytechnic Institute Worcester USA Program of Data Science University of Massachusetts Dartmouth USA Old Dominion University - Thomas Jefferson National Accelerator Facility Joint Institute on Advanced Computing for Environmental Studies Portsmouth USA Miner School of Computer and Information Sciences University of Massachusetts Lowell Lowell USA

Recent progress in the field of high-fidelity image synthesis using GANs has shown appealing outcomes, which motivates a series of successful image super-resolution (SR) works. However, most GAN-based SR models apply plain GAN models and are cumbersome compared to CNN-based SR models. In addition, the very advantage of conditional GAN has not yet been explored under the context of SR. In this paper, we develop a lightweight SR-BigGAN with priors for single-image super-resolution (SISR). First, our new model is an extension of BigGAN tailored to deep SR pipeline, retaining both generator and discriminator architectures, but with modifications to accommodate SR tasks. Second, prior knowledge defined as class labels from the low-resolution images, is fully leveraged through the conditional generative model to refine the SR process. Third, the lightweight nature of the model is achieved through knowledge distillation, focusing on reduced computational complexity and memory usage, making it the first practice of this kind in GAN-based SISR modeling. Extensive experiments on DIV2K, Pascal, mini-ImageNet, and SR benchmarks including Set5 and Set14 in an attempt to compare the Structural Similarity (SSIM), Peak Signal-to-Noise Ratio (PSNR) with the state-of-the-art models have shown appealing results. Our model achieves an average PSNR of 34.99 and SSIM of 0.791 across these datasets, demonstrating quantitative improvements over existing methods. The generated high-resolution images offer both perceptual enhancement and improved classification results. Additionally, explicit comparisons with GAN-based SR techniques such as ESRGAN and SRGAN highlight the superiority of our approach in both fidelity and efficiency. In particular, we achieve an average of $$\hbox{PSNR}=34.99$$ and $$\hbox{SSIM}=0.791$$ on several testing datasets.

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Combined Effect of Concept Drift and Class Imbalance on Model Performance During Stream Classification

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Computers, Materials & Continua 2023年第4期75卷 1827-1845页

作者： Abdul Sattar Palli Jafreezal Jaafar Manzoor Ahmed Hashmani Heitor Murilo Gomes Aeshah Alsughayyir Abdul Rehman Gilal Department of Computer and Information Sciences Universiti Teknologi PETRONAS(UTP)Seri Iskandar32610Malaysia Centre for Research in Data Science UTPPerak32610Malaysia High Performance Cloud Computing Centre(HPC3) UTPPerak32610Malaysia School of Engineering and Computer Science Victoria University of WellingtonWellington6012New Zealand AI Institute University of Waikato WellingtonHamilton3240New Zealand Anti-Narcotics Force Ministry of Narcotics ControlIslamabad46000Pakistan College of Computer Science and Engineering Taibah UniversityMadinah42353Saudi Arabia

Every application in a smart city environment like the smart grid,health monitoring, security, and surveillance generates non-stationary datastreams. Due to such nature, the statistical properties of data changes overtime, leading to class imbalance and concept drift issues. Both these issuescause model performance degradation. Most of the current work has beenfocused on developing an ensemble strategy by training a new classifier on thelatest data to resolve the issue. These techniques suffer while training the newclassifier if the data is imbalanced. Also, the class imbalance ratio may changegreatly from one input stream to another, making the problem more *** existing solutions proposed for addressing the combined issue of classimbalance and concept drift are lacking in understating of correlation of oneproblem with the other. This work studies the association between conceptdrift and class imbalance ratio and then demonstrates how changes in classimbalance ratio along with concept drift affect the classifier’s *** analyzed the effect of both the issues on minority and majority classesindividually. To do this, we conducted experiments on benchmark datasetsusing state-of-the-art classifiers especially designed for data stream ***, recall, F1 score, and geometric mean were used to measure theperformance. Our findings show that when both class imbalance and conceptdrift problems occur together the performance can decrease up to 15%. Ourresults also show that the increase in the imbalance ratio can cause a 10% to15% decrease in the precision scores of both minority and majority *** study findings may help in designing intelligent and adaptive solutionsthat can cope with the challenges of non-stationary data streams like conceptdrift and class imbalance.

关键词： Classification data streams class imbalance concept drift class imbalance ratio

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RAKG:Document-level Retrieval Augmented Knowledge Graph Construction

arXiv

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arXiv 2025年

作者： Zhang, Hairong Si, Jiaheng Yan, Guohang Qi, Boyuan Cai, Pinlong Mao, Song Wang, Ding Shi, Botian Shanghai Artificial Intelligence Laboratory China School of Statistics and Data Science Nankai University Tianjin China Faculty of Computing Harbin Institute of Technology China Computer School Beijing Information Science and Technology University China

With the rise of knowledge graph based retrieval-augmented generation (RAG) techniques such as GraphRAG and Pike-RAG, the role of knowledge graphs in enhancing the reasoning capabilities of large language models (LLMs) has become increasingly prominent. However, traditional Knowledge Graph Construction (KGC) methods face challenges like complex entity disambiguation, rigid schema definition, and insufficient cross-document knowledge integration. This paper focuses on the task of automatic document-level knowledge graph construction. It proposes the Document-level Retrieval Augmented Knowledge Graph Construction (RAKG) framework. RAKG extracts pre-entities from text chunks and utilizes these pre-entities as queries for RAG, effectively addressing the issue of long-context forgetting in LLMs and reducing the complexity of Coreference Resolution. In contrast to conventional KGC methods, RAKG more effectively captures global information and the interconnections among disparate nodes, thereby enhancing the overall performance of the model. Additionally, we transfer the RAG evaluation framework to the KGC field and filter and evaluate the generated knowledge graphs, thereby avoiding incorrectly generated entities and relationships caused by hallucinations in LLMs. We further developed the MINE dataset by constructing standard knowledge graphs for each article and experimentally validated the performance of RAKG. The results show that RAKG achieves an accuracy of 95.91% on the MINE dataset, a 6.2 % point improvement over the current best baseline, GraphRAG (89.71%). The code is available at https://***/LMMApplication/RAKG. Copyright © 2025, The Authors. All rights reserved.

关键词： Graphic methods

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High Quality Global Routing Algorithm Based on Multi-Stage Optimization

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Jisuanji Fuzhu Sheji Yu Tuxingxue Xuebao/Journal of Computer-Aided Design and Computer Graphics 2024年第4期36卷 607-614页

作者： Genggeng, Liu Zhenyu, Pei Ning, Xu College of Computer and Data Science Fuzhou University Fuzhou350116 China State Key Laboratory of Computer Architecture Institute of Computing Technology Chinese Academy of Sciences Beijing100190 China School of Information Engineering Wuhan University of Technology Wuhan430070 China

Global routing is a crucial step in VLSI physical design. To address the problems that the low utilization rate of capacity and easy to fall into local optimum in the existing global routing algorithms, a high-quality global routing algorithm based on multi-stage optimization is proposed. First, the virtual capacity dynamic adjustment strategy is introduced in the pre-connection stage and the routing reorganization stage to improve the utilization rate of capacity. Then, in the routing reorganization stage, the routing subarea adaptive expansion strategy is proposed to narrow the search space and improve the routing efficiency. Finally, the A* algorithm heuristic search is combined with the global search capability of the particle swarm algorithm to avoid the algorithm to choose the shortest path in the routing process, and thus avoid the algorithm to fall into the local optimum. Experimental results comparing with existing global routing algorithms on ISPD benchmark circuits show that the routing results of proposed algorithm reduce the wirelength by 6.0% to 22.8%. © 2024 institute of computing Technology. All rights reserved.

关键词： Routing algorithms

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Expressive Completeness of Two-Variable First-Order Logic with Counting for First-Order Logic Queries on Rooted Unranked Trees

Expressive Completeness of Two-Variable First-Order Logic wi...

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Annual Symposium on Logic in Computer science

作者： Jelle Hellings Marc Gyssens Jan Van Den Bussche Dirk Van Gucht Department of Computing and Software McMaster University Hamilton Canada Data Science Institute Hasselt University Diepenbeek Belgium Luddy School of Informatics Computing and Engineering Indiana University Bloomington IN USA

We consider the class of finite, rooted, unranked, unordered, node-labeled trees. Such trees are represented as structures with only the parent-child relation, in addition to any number of unary predicates for node labels. We prove that every unary first-order query over the considered class of trees is already expressible in two-variable first-order logic with counting. Somewhat to our surprise, we have not seen this result being conjectured in the extensive literature on logics for trees. Our proof is based on a global variant of local equivalence notions on nodes of trees. This variant applies to entire trees, and involves counting ancestors of locally equivalent nodes.

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Research on patent recommendation method based on graph neural network 26

Research on patent recommendation method based on graph neur...

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26th International Conference on Computer Supported Cooperative Work in Design, CSCWD 2023

作者： Zhang, Enchao Liu, Weidong Liu, Shi Cao, Yan Institute of Scientific and Technical Information of China China College of Computer Science Inner Mongolia University China Natl. Loc. Jt. Eng. Research Center of Intelligent Information Processing Technology for Mongolian China Inner Mongolia Key Laboratory of Social Computing and Data Processing China Inner Mongolia Engineering Laboratory for Big Data Analysis Technology China

ISBN: (纸本)9798350331684

Intellectual property transactions have shown a strong growth momentum in recent years, but the patent transaction market has been plagued by the matching degree of consumers and sellers, resulting in frequent problems such as low patent transformation efficiency and poor transaction quality. This paper proposes a method of recommending patents to consumers by experts to improve the environment of patent transactions. Through the analysis of the past transaction information of the patent, the effective path information of the target is extracted. The graph neural network is used to describe the characteristics and semantics among experts, patents and consumers, and then capture the potential weight among them through the common attention mechanism, and then dynamically integrate them to predict the occurrence of recommendation behavior. The paper makes reasonable use of social information and expert information in the transaction, which significantly improves the rationality and accuracy of expert recommendation. © 2023 IEEE.

关键词： Patents and inventions

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Strictly positive definite kernels on compact Riemannian manifolds

arXiv

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arXiv 2023年

作者： Guella, Jean Carlo Jäger, Janin Unicamp Institute of Mathematics Statistics and Scientific Computing Campinas Brazil KU Eichstätt-Ingolstadt Mathematisches Institute for machine learning and data science Ingolstadt Germany

The paper studies strictly positive definite kernels on compact Riemannian manifolds. We state new conditions to ensure strict positive definiteness for general kernels and kernels with certain convolutional structure. We also state conditions for such kernels on product manifolds. As an example conditions for products of two-point homogeneous spaces are *** Codes 33B10, 33C20, 42A16, 46E22, 65D05 Copyright © 2023, The Authors. All rights reserved.

关键词： Geometry

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Image Enhancement on Underwater Images for Protozoan White Spot Fish Disease Detection 5

Image Enhancement on Underwater Images for Protozoan White S...

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5th International Conference on Intelligent Systems and Computer Vision, ISCV 2022

作者： Azhar, Amiera Syazlin Binti Md Harun, Nor Hazlyna Binti Yusoff, Nooraini Binti Hassan, Mohamad Ghozali Bin Chu, Kua Beng Universiti Utara Malaysia Data Science Research Lab School of Computing Kedah Malaysia Universiti Malaysia Kelantan Department of Data Science Kelantan Malaysia Universiti Utara Malaysia School of Technology Management and Logistics Kedah Malaysia Fish Research Institute National Fish Health Research Centre Department of Fisheries Malaysia Penang Malaysia

ISBN: (纸本)9781665495585

White spot disease in mariculture fish could lead to major economic losses if the disease could not be contained in early stage. With the advent of current technology, image processing is believed could be a key contributor to early prevention of fish disease specifically protozoan white spot disease in early stage via the use of image enhancement technique on underwater images. This alternative could contribute to aquaculture sector by preventing the disease worst-case scenarios. Hence, this paper demonstrates the comparison between variety image enhancement techniques to find the best method in addressing the issues related to underwater images. The image enhancement techniques include auto-level colour correction and contrast limited adaptive histogram equalization (CLAHE) have been tested. The techniques alone and the combination of both has been compared and successfully improved underwater image quality. Hence, the idea of explore a combination of multiple techniques in order to create a superior underwater image quality could be done to ease the process of detecting protozoan white spot disease. © 2022 IEEE.

关键词： Image enhancement

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A Survey of Integrating Generative Artificial Intelligence and 6G Mobile Services: Architectures, Solutions, Technologies and Outlooks

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IEEE Transactions on Cognitive Communications and Networking 2025年第3期11卷 1334-1356页

作者： Liu, Yi-Jing Du, Hongyang Xu, Xinyi Zhang, Ruichen Feng, Gang Cao, Bin Niyato, Dusit In Kim, Dong Jamalipour, Abbas Ben Letaief, Khaled Tafazolli, Rahim University of Electronic Science and Technology of China National Key Laboratory on Wireless Communications Chengdu610054 China University of Hong Kong Department of Electrical and Electronic Engineering Hong Kong Nanyang Technological University College of Computing and Data Science Jurong West Singapore Beijing University of Posts and Telecommunications State Key Laboratory of Networking and Switching Technology Beijing100876 China Sungkyunkwan University Department of Electrical and Computer Engineering Suwon03063 Korea Republic of University of Sydney School of Electrical and Computer Engineering SydneyNSW2006 Australia Hong Kong University of Science and Technology Department of Electronic and Computer Engineering Hong Kong Institute for Communication Systems School of Computer Science and Electronic Engineering University of Surrey GuildfordGU2 7XH United Kingdom

Generative artificial intelligence (GenAI) is rapidly driving a new phase of artificial intelligence revolution, marked by various applications such as ChatGPT, Sora and DeepSeek. With powerful capabilities in content generation, personalization and user interaction, GenAI can drive significant enhancements in various mobile services to satisfy individual user preferences. Meanwhile, mobile services offer substantial support for accessibility, functionality and intelligence of GenAI applications. However, current research on integrating GenAI and mobile services remains nascent, lacking necessary theoretical and technical support. In this survey, we break down a new vision to guide research on integrating GenAI and mobile services, aiming to improve 6G intelligence levels and enable 6G mobile networks to support the integration of GenAI and mobile services effectively. We begin by examining the necessity of integrating GenAI with 6G mobile services and exploring potential learning architectures of GenAI models within mobile networks. Next, we analyze potential solutions from two perspectives: how 6G mobile networks empower GenAI and how GenAI enables mobile services. We subsequently explore a promising use case, along with essential features and techniques, to facilitate the integration of GenAI and mobile services. Finally, we discuss future research directions, aiming at realizing intelligent 6G networks. © 2015 IEEE.

关键词： Generative adversarial networks

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