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39th Annual AAAI Conference on Artificial Intelligence, AAAI 2025

作者： Wang, Ziquan Xia, Mingxuan Ren, Xiangyu Zhou, Jiaqing Lyu, Gengyu Hu, Tianlei Wang, Haobo School of Software Technology Zhejiang University China Hangzhou High-Tech Zone (Binjiang) Institute of Blockchain and Data Security China College of Computer and Control Engineering Northeast Forestry University China ByteDance Hangzhou China College of Computer Science Beijing University of Technology China

ISBN: (纸本)157735897X

Multi-instance multi-label classification (MIML) is a fundamental task in machine learning, where each data sample comprises a bag containing several instances and multiple binary labels. Despite its wide applications, the data collection process involves matching multiple instances and labels, typically resulting in high annotation costs. In this paper, we study a novel yet practical crowdsourced multi-instance multi-label classification (CMIML) setup, where labels are collected from multiple crowd sources. To address this problem, we first propose a novel data generation process for CMIML, i.e., cross-label transition, where cross-label annotation error is more likely to appear rather than previous single-label transition assumption, due to the inherent similarity of localized instances from different classes. Then, we formally define the cross-label transition by cross-label transition matrices which are dependent across classes. Subsequently, we establish the first unbiased risk estimator for CMIML and further improve it through aggregation techniques, along with a rigorous generalization error bound. We also provide a practical implementation of cross-label transition matrix estimation. Comprehensive experiments on six benchmark datasets under various scenarios demonstrate that our algorithm outperforms the baselines by a large margin, validating its effectiveness in handling the CMIML problem. © 2025, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

关键词： Nameplates

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A comprehensive survey on shadow removal from document images: datasets, methods, and opportunities

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Vicinagearth 2025年第1期2卷 1-18页

作者： Wang, Bingshu Li, Changping Zou, Wenbin Zhang, Yongjun Chen, Xuhang Chen, C.L. Philip School of Software Northwestern Polytechnical University Xi’an China Guangdong Provincial Key Laboratory of Intelligent Information Processing & Shenzhen Key Laboratory of Media Security Shenzhen University Shenzhen China Guangdong Key Laboratory of Intelligent Information Processing College of Electronics and Information Engineering Shenzhen University Shenzhen China Yongjun Zhang is with the State Key Laboratory of Public Big Data College of Computer Science and Technology Guizhou University Guiyang China School of Computer Science and Engineering Huizhou University Huizhou China School of Computer Science and Engineering South China University of Technology and Pazhou Lab Guangzhou China

With the rapid development of document digitization, people have become accustomed to capturing and processing documents using electronic devices such as smartphones. However, the captured document images often suffer from issues like shadows and noise due to environmental factors, which can affect their readability. To improve the quality of captured document images, researchers have proposed a series of models or frameworks and applied them in distinct scenarios such as image enhancement, and document information extraction. In this paper, we primarily focus on shadow removal methods and open-source datasets. We concentrate on recent advancements in this area, first organizing and analyzing nine available datasets. Then, the methods are categorized into conventional methods and neural network-based methods. Conventional methods use manually designed features and include shadow map-based approaches and illumination-based approaches. Neural network-based methods automatically generate features from data and are divided into single-stage approaches and multi-stage approaches. We detail representative algorithms and briefly describe some typical techniques. Finally, we analyze and discuss experimental results, identifying the limitations of datasets and methods. Future research directions are discussed, and nine suggestions for shadow removal from document images are proposed. To our knowledge, this is the first survey of shadow removal methods and related datasets from document images.

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The Mechanical Properties of Tcp Phase of Rapidly Cooled Molybdenum

SSRN

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SSRN 2023年

作者： Fu, Mingshao Zheng, Quan Tian, Zean Institute of Advanced Optoelectronic Materials and Technology School of Big Data and Information Engineering Guizhou University Guiyang550025 China College of Computer Science and Electronic Engineering Hunan University Changsha410082 China

The rapidly-cooling of pure molybdenum (Mo) at 1010 Ks−1 and the uniaxial compression of the solid at a strain rate of 2×10 s−1 were studied by molecular dynamics simulation;then the structure evolution was investigated in terms of pair distribution function and the largest standard cluster analysis. It is found that Mo melt was cooled into a complex crystal with multiple characteristic lengths. Further analysis revealed that it is a mixture topological close packing (TCP) crystal composed of the dominated A15 phase (Mo-A15) and the less Z phase, with H phase as twin grain boundary. The Phonon spectrum, potential energy, and cohesion energy revealed that Mo-A15 is stable slightly inferior to Mo-bcc. Compared to the prevailing bcc Mo, Mo-A15 holds much higher Young's modulus, ultimate tensile strength, and Yield strength, being a representative material for barrier layer with high hardness. © 2023, The Authors. All rights reserved.

关键词： Molecular dynamics

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A Topology Independent Fast Rerouting Scheme with Hop-by-Hop Forwarding Can Achieve a Full Protection

SSRN

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SSRN 2022年

作者： Geng, Haijun Meng, Zhuo Zhang, Han Shi, Xingang Wang, Zhiliang Yin, Xia Chen, Meng Guo, Yingya The School of Automation and Software Engineering Shanxi University School of Computer and Information Technology China The Institute for Network Sciences and Cyberspace Tsinghua University China The Department of Computer Science and Technology Tsinghua University China The School of Big Data and Software Engineering Chongqing University China The College of Computer and Data Science Fuzhou University China

To support critical applications or guarantee a high quality of service, Internet Service Providers (ISPs) generally deploy a technique called routing protection algorithm for local fast rerouting. Alternate next hops are stored alongside with the default next hops in a router’s forwarding table, and can be immediately activated to invoke a loop free repair path in face of link or node failures, or used for multipath transmission. However, the existing routing protection methods either cannot protect all possible single-failure scenarios in the network or needs the assistance of an auxiliary address. In response to the above problems, the present study proposes a single-failure routing protection algorithm based on hop by hop routing (SPA). The algorithm has the following three advantages: (1) supports hop-by-hop forwarding, (2) supports incremental deployment, and (3) can protect all possible singlefailure scenarios in the network as long as network keep connected. Extensive simulations show that, compared to the state-of-the-art schemes which are adopted by the industry, SPA not only can handle arbitrary single-failure situations in the network but also has a small path stretch. These advantages make SPA a good candidate for practical solution for IP fast reroute when network failure occurs. © 2022, The Authors. All rights reserved.

关键词： Internet service providers

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A Large Deviation Algorithm for the Thresholding Bandit Problem

SSRN

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SSRN 2023年

作者： He, Yulin Zhang, Manjing Liu, Guangwu Fournier-Viger, Philippe Dai, Shan Huang, Joshua Zhexue Shenzhen518107 China Department of Management Sciences City University of Hong Kong 518057 Hong Kong College of Computer Science & Software Engineering Shenzhen University Shenzhen518060 China Shenzhen Research Institute of Big Data Shenzhen518172 China

The thresholding bandit (TB) problem is a popular sequential decision-making problem, which aims at identifying the systems whose means are greater than a threshold. The TB problem is a variant of the multi-armed bandit (MAB) problem, which seeks to maximize the total reward by balancing the trade-off between exploration and exploitation. This paper presents a parameter-free policy for the TB problem based on the large-deviation theory, and proposes a large-deviation (LD) algorithm to solve the TB problem using an optimal allocation rule. We theoretically show that the LD algorithm is asymptotically optimal, and its probability of incorrect identification converges more quickly than the existing algorithms as the sample size approaches to infinity. Furthermore, to improve the robustness of the LD algorithm for optimization problems with small sample sizes, a decay rate-based approximate large-deviation (ALD) algorithm is proposed that takes the sample means and sample variances into account in a direct and simple way. Extensive experiments are conducted to validate the effectiveness of the LD and ALD algorithms. Simulation results indicate that both LD and ALD are viable solutions to handle the TB problem. © 2023, The Authors. All rights reserved.

关键词： Decision making

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Locating what you need: towards adapting diffusion models to OOD concepts in-the-wild 24

Locating what you need: towards adapting diffusion models to...

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Proceedings of the 38th International Conference on Neural Information Processing Systems

作者： Jianan Yang Chenchao Gao Zhiqing Xiao Junbo Zhao Sai Wu Gang Chen Haobo Wang College of Computer Science and Technology Zhejiang University and Hangzhou High-Tech Zone (Binjiang) Institute of Blockchain and Data Security International School of Information Science and Engineering Dalian University of Technology and Hangzhou High-Tech Zone (Binjiang) Institute of Blockchain and Data Security School of Software Technology Zhejiang University and Hangzhou High-Tech Zone (Binjiang) Institute of Blockchain and Data Security

ISBN: (纸本)9798331314385

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.

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VMeta: A QoS dataset for Metaverse Services 6th

VMeta: A QoS Dataset for Metaverse Services

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6th International Conference on Blockchain and Trustworthy Systems, BlockSys 2024

作者： Ge, Zhiyuan Jin, Huiying Dong, Hai Ji, Shunhui Li, Jiajia Yang, Shuhan Wang, Qi Zhang, Pengcheng Key Laboratory of Water Big Data Technology of Ministry of Water Resources Hohai University Nanjing210000 China College of Computer Science and Software Engineering Hohai University Nanjing210000 China School of Computer Science Nanjing University of Posts and Telecommunications Nanjing210000 China School of Computing Technologies RMIT University Melbourne3000 Australia

ISBN: (纸本)9789819614134

The metaverse is undergoing repaid evolution with metaverse services emerging as a significant focal point of research across virtual reality, augmented reality, and metaverse social networking. However, a key challenge lies in the scarcity of datasets that accurately represent metaverse services, impeding the advancement of metaverse service technologies and applications. To address this challenge, we have categorized metaverse services into virtual scene services and meta-services. Subsequently, we introduced a metaverse service dataset called "VMeta". VMeta encompasses comprehensive information about virtual scene services and meta-services, including essential QoS (Quality of Service) data. This dataset serves as a valuable resource for researchers exploring various aspects of metaverse services, with the ultimate goal of enhancing user satisfaction in the metaverse. We have conducted evaluations and tests on VMeta to optimize metaverse QoS. The results demonstrate its substantial value for metaverse service research. Our datasets are available at https://***/Gelieziman/VMeta. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Virtual environments

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Radio Map Estimation via Latent Domain Plug-and-Play Denoising

arXiv

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arXiv 2025年

作者： Xu, Le Cheng, Lei Chen, Junting Pu, Wenqiang Fu, Xiao The School of Electrical Engineering and Computer Science Oregon State University OR United States The College of Information Science and Electronic Engineering Zhejiang University Hangzhou China The Chinese University of Hong Kong Shenzhen China The Shenzhen Research Institute of Big Data The Chinese University of Hong Kong Shenzhen China

Radio map estimation (RME), also known as spectrum cartography, aims to reconstruct the strength of radio interference across different domains (e.g., space and frequency) from sparsely sampled measurements. To tackle this typical inverse problem, state-of-the-art RME methods rely on handcrafted or data-driven structural information of radio maps. However, the former often struggles to model complex radio frequency (RF) environments and the latter requires excessive training-making it hard to quickly adapt to in situ sensing tasks. This work presents a spatio-spectral RME approach based on plug-and-play (PnP) denoising, a technique from computational imaging. The idea is to leverage the observation that the denoising operations of signals like natural images and radio maps are similar-despite the nontrivial differences of the signals themselves. Hence, sophisticated denoisers designed for or learned from natural images can be directly employed to assist RME, avoiding using radio map data for training. Unlike conventional PnP methods that operate directly in the data domain, the proposed method exploits the underlying physical structure of radio maps and proposes an ADMM algorithm that denoises in a latent domain. This design significantly improves computational efficiency and enhances noise robustness. Theoretical aspects, e.g., recoverability of the complete radio map and convergence of the ADMM algorithm are analyzed. Synthetic and real data experiments are conducted to demonstrate the effectiveness of our approach. © 2025, CC BY-NC-ND.

关键词： Inverse problems

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FDSR-INT: A Flexible On-Demand In-Band Telemetry Approach for Aerial Computing Networks

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IEEE Internet of Things Journal 2025年

作者： Xu, Jinghan Xu, Xiaolong Zhao, Juan Gao, Honghao Nanjing University of Posts and Telecommunications School of Computer Science Nanjing210023 China Nanjing University of Posts and Telecommunications Jiangsu Key Laboratory of Big Data Security and Intelligent Processing The School of Computer Science Nanjing210023 China Jinling Institute of Technology School of Computer Science Nanjing211169 China Shanghai University School of Computer Engineering and Science Shanghai200444 China Gachon University College of Future Industry Gyeonggi-Do 461-701 Korea Republic of

In-band network telemetry (INT) is a new network measurement technique that provides real-time, fine-grained packet-level network measurements. However, standard INT lacks the flexibility to perform configurable on-demand network measurements. In this work, we propose a flexible on-demand network measurement mechanism (FDSR-INT) based on a dual bitmap by combining the programmability of segment routing based on IPv6 (SRv6) and the telemetry efficiency of the INT to achieve customizable network measurements. By designing the dual bitmap, the telemetry information of personalized probe nodes is supported, and the telemetry efficiency is improved. SRv6 is employed to direct measurement probe packets, ensuring coverage of a specified set of network targets. Its inherent programmability enables INT to conduct customized per-hop network measurements. The designed flexible INT field structure, which appends traceability information along with telemetry information, reduces the information traceability overhead in the control plane and improves the network compatibility of the mechanism. We solve the optimal probing path by solving a traveler problem in an auxiliary graph and design a greedy pathcutting algorithm to maximize the number of nodes for single packet probing while satisfying the packet length constraints to improve the success rate of the probing task. Finally, we implemented FDSR-INT using P4 and verified its performance experimentally in the constructed *** integrated simulation dynamic environment. FDSR-INT saves 30% of the data plane bandwidth and 54% of the data plane bandwidth on *** interfaces compared with SONM-SR-INT, etc. Furthermore, it has low control plane processing overhead and probe path transmission overhead. © 2014 IEEE.

关键词： Bandwidth

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GRAFRAUD: Fraud Detection using Graph databases and Neural Networks

GRAFRAUD: Fraud Detection using Graph Databases and Neural N...

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2023 4th International Informatics and software engineering Conference (IISEC)

作者： Alperen Sayar Şuayip Arslan Ajeet Singh Raina Seyit Ertuğrul Tuna Çakar Data Science Tam Finans R&D Istanbul Türkiye Big Data and Data Governance Massachusetts Institute of Technology Massachusetts USA Docker Containerized Software Docker Inc. California USA Computer Engineering MEF University Istanbul Türkiye

The issue of fraud has become a significant concern for many companies, particularly in the finance sector, but the traditional methods of detecting fraud are no longer adequate. Innovative technologies are necessary to identify complex fraudulent activities, and RedisGraph, a high-performance graph database, may offer a solution. With the assistance of neural networks, RedisGraph can accurately and efficiently detect fraudulent transactions in vast and intricate environments. Companies typically use a combination of Python and Oracle databases to design fraud detection systems. which provide robust data management and real time AI processing capabilities. These technologies allow to create fraud detection systems that can determine fraudulent activities in real-time. But according to advancements of fraud methods only using of these systems not efficient nowadays. This article presents a proof of concept based on an essential use case of RedisGraph-powered neural networks in detecting financial fraud. It demonstrates the value of carefully employing Python and Oracle database to construct and deploy real-time systems that can efficiently detect fraudulent activities.

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