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4th International Workshop on data meets Ontologies in Explainable AI, DAO-XAI 2024

作者： Horsch, Martin T. Chiacchiera, Silvia Todorov, Ilian T. Correia, Ana Teresa Dey, Aditya Konchakova, Natalia A. Scholze, Sebastian Stephan, Simon Tøndel, Kristin Sarkar, Arkopaul Karray, M. Hedi Al Machot, Fadi Schembera, Björn Norwegian University of Life Sciences Department of Data Science Postboks 5003 Ås1432 Norway UKRI Science and Technology Facilities Council Scientific Computing Department DaresburyWA4 4AD United Kingdom ATB Institut für Angewandte Systemtechnik Bremen GmbH Wiener Str. 1 Bremen28359 Germany Helmholtz-Zentrum Hereon Institute of Surface Science Max-Planck-Str. 1 Geesthacht21502 Germany RPTU Kaiserslautern Laboratory of Engineering Thermodynamics Kaiserslautern67663 Germany University of Technology Tarbes Occitanie Pyrénées Production Engineering Lab. 47 av. d’Azereix Tarbes France University of Stuttgart Institute of Applied Analysis and Numerical Simulation Stuttgart70569 Germany

This position paper reports on the initial discussions within the Knowledge Graph Alliance’s working group on explainable-AI-ready data and metadata principles, which was created in March 2024. At present, we are taking initial steps toward capturing core concepts related to explanation, grounding, reliance, and trust;the scope also extends to potential dual notions such as explainability, verifiability/reproducibility, reliability, and trustworthiness. These initial steps consist in reviewing core concepts as they are discussed in the literature and exploring what could be practically useful definitions of these most central concepts. One of the conclusions is that the metadata standards will need to be suitable for documenting three kinds of grounding: Grounding of knowledge, grounding of reliance, and grounding of trust. Pre-existing metadata standards at the mid and domain level are presently undergoing a redesign in order to become more modular, computationally tractable, intelligible to humans, and adjustable, which will be needed as we continue our work toward actionable recommendations. The development of this system of lite (OWL 2 EL) ontologies, called MSO-EM: Ontologies for modelling, simulation, optimization (MSO) and epistemic metadata (EM), is carried out on a public repository. © 2024 Copyright for this paper by its authors.

关键词： Applied ontology epistemic metadata explainable-AI-ready (XAIR) ontology redesign reliability reproducibility

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What if? Causal Machine Learning in Supply Chain Risk Management

arXiv

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arXiv 2024年

作者： Wyrembek, Mateusz Baryannis, George Brintrup, Alexandra Department of Logistics Poznań University of Economics and Business Poland Department of Computer Science University of Huddersfield United Kingdom Supply Chain AI Lab University of Cambridge United Kingdom Data Centric Engineering Alan Turing Institute United Kingdom

The penultimate goal for developing machine learning models in supply chain management is to make optimal interventions. However, most machine learning models identify correlations in data rather than inferring causation, making it difficult to systematically plan for better outcomes. In this article, we propose and evaluate the use of causal machine learning for developing supply chain risk intervention models, and demonstrate its use with a case study in supply chain risk management in the maritime engineering sector. Our findings highlight that causal machine learning enhances decision-making processes by identifying changes that can be achieved under different supply chain interventions, allowing "what-if" scenario planning. We therefore propose different machine learning developmental pathways for for predicting risk, and planning for interventions to minimise risk and outline key steps for supply chain researchers to explore causal machine learning. © 2024, CC BY.

关键词： Adversarial machine learning

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NaFV-Net: An Adversarial Four-view Network for Mammogram Classification 39

NaFV-Net: An Adversarial Four-view Network for Mammogram Cla...

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39th Annual AAAI Conference on Artificial Intelligence, AAAI 2025

作者： Lu, Feng Hou, Yuxiang Li, Wei Yang, Xiangying Zheng, Haibo Luo, Wenxi Chen, Leqing Cao, Yuyang Liao, Xiaofei Zhang, Yu Yang, Fan Zomaya, Albert Jin, Hai 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 Australia-China Joint Research Centre for Energy Informatics and Demand Response Technologies Centre for Distributed and High Performance Computing School of Computer Science University of Sydney Australia Tongji Hospital Tongji Medical College Huazhong University of Science and Technology China

ISBN: (纸本)157735897X

Breast cancer remains a leading cause of mortality among women, with millions of new cases diagnosed annually. Early detection through screening is crucial. Using neural networks to improve the accuracy of breast cancer screening has become increasingly important. In accordance with radiologists’ practices, we propose using images from the unaffected side to create adversarial samples with critical medical implications in our adversarial learning process. By introducing beneficial perturbations, this method aims to reduce overconfidence and improve the precision and robustness of breast cancer classification. Our proposed framework is an adversarial four-view classification network (NaFV-Net) incorporating images from both affected and unaffected perspectives. By comprehensively capturing local and global information and implementing adversarial learning from four mammography views, this framework allows for the fusion of features and the integration of medical principles and radiologist evaluation techniques, thus facilitating the accurate identification and characterization of breast tissues. Extensive experiments have shown the high effectiveness of our model in accurately distinguishing between benign and malignant findings, demonstrating state-of-the-art classification performance on both internal and public datasets. Copyright © 2025, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

关键词： Mammography

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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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Unifying and Improving Graph Convolutional Neural Networks with Wavelet Denoising Filters 23

Unifying and Improving Graph Convolutional Neural Networks w...

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32nd ACM World Wide Web Conference, WWW 2023

作者： Wan, Liangtian Li, Xiaona Han, Huijin Yan, Xiaoran Sun, Lu Ning, Zhaolong Xia, Feng Key Laboratory for Ubiquitous Network and Service Software of Liaoning Province School of Software Dalian University of Technology Dalian China Research Center of Big Data Intelligence Research Institute of Artificial Intelligence Zhejiang Lab Hangzhou China Department of Communication Engineering Institute of Information Science Technology Dalian Maritime University Dalian China School of Communication and Information Engineering Chongqing University of Posts and Telecommunications Chongqing China School of Computing Technologies Rmit University Melbourne Australia

ISBN: (纸本)9781450394161

Graph convolutional neural network (GCN) is a powerful deep learning framework for network data. However, variants of graph neural architectures can lead to drastically different performance on different tasks. Model comparison calls for a unifying framework with interpretability and principled experimental procedures. Based on the theories from graph signal processing (GSP), we show that GCN's capability is fundamentally limited by the uncertainty principle, and wavelets provide a controllable trade-off between local and global information. We adapt wavelet denoising filters to the graph domain, unifying popular variants of GCN under a common interpretable mathematical framework. Furthermore, we propose WaveThresh and WaveShrink which are novel GCN models based on proven denoising filters from the signal processing literature. Empirically, we evaluate our models and other popular GCNs under a more principled procedure and analyze how trade-offs between local and global graph signals can lead to better performance in different datasets. © 2023 ACM.

关键词： Convolution

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Multi-Order Loss Functions For Accelerating Unsteady Flow Simulations with Physics-Based AI

Multi-Order Loss Functions For Accelerating Unsteady Flow Si...

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Artificial Intelligence (CAI), IEEE Conference on

作者： Wei Xian Lim Naheed Anjum Arafat Wai Lee Chan Wai-Kin Adams Kong Rolls-Royce@NTU Corporate Lab Nanyang Technological University (NTUsg) Singapore School of Mechanical and Aerospace Engineering (MAE) Nanyang Technological University (NTUsg) Singapore College of Computing and Data Science (CCDS) Nanyang Technological University (NTUsg) Singapore

ISBN: (数字)9798350354096

ISBN: (纸本)9798350354102

Studies show that artificial intelligence (AI) with embedded physics solvers has improved the accuracy of predictions on various physics problems, especially those associated with fluid dynamics. The crucial element in optimizing weight training for estimating flow fields within the AI network lies in the choice of the loss function. In addressing regression-type problems, particularly those involving the temporal evolution of flow fields, the mean square error (MSE) loss function is commonly employed at the current and single time step. However, an issue arises in existing methodologies that utilize MSE-based loss functions with single-time step information for predicting unsteady flow. Most of these approaches overlook the significance of incorporating the temporal history of the flow, a factor that cannot be disregarded in the context of numerical solvers. Hence, in this work, a physics-based AI (PbAI) method with higher-order loss functions is applied to unsteady scenarios, in particular to two distinct turbulent flows where a multitude of fine structures is present, namely, forced and decaying turbulence. Direct numerical simulations on uniform Cartesian grids are conducted to simulate these scenarios, generating two distinct datasets for training and inference. Each dataset comprises 32 randomly initialized conditions spanning 4, 848 time steps for each turbulent flow type. Five distinct models are devised, incorporating features such as rollouts from coarse numerical solvers and temporal considerations in the loss function calculation. The constructed PbAI models demonstrate consistent improvements in predictive performance over the entire temporal domain. These findings are further corroborated through vorticity correlation analyses. The empirical result demonstrates that the accuracy of the baseline case improves by up to 48% and 30% for forced and decaying turbulence, respectively. These results significantly underscore the importance of the temporal histo

关键词： Training Accuracy Computational modeling Neural networks Predictive models Network architecture Numerical models

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Generalizable Black-Box Adversarial Attack with Meta Learning

arXiv

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arXiv 2023年

作者： Yin, Fei Zhang, Yong Wu, Baoyuan Feng, Yan Zhang, Jingyi Fan, Yanbo Yang, Yujiu Tsinghua Shenzhen International Graduate School Tsinghua University Beijing100190 China The School of Data Science Shenzhen Research Institute of Big Data Chinese University of Hong Kong Shenzhen518172 China Tencent AI Lab Guangdong Shenzhen518057 China The Center for Future Media The School of Computer Science and Engineering University of Electronic Science and Technology of China Chengdu610056 China

In the scenario of black-box adversarial attack, the target model's parameters are unknown, and the attacker aims to find a successful adversarial perturbation based on query feedback under a query budget. Due to the limited feedback information, existing query-based black-box attack methods often require many queries for attacking each benign example. To reduce query cost, we propose to utilize the feedback information across historical attacks, dubbed example-level adversarial transferability. Specifically, by treating the attack on each benign example as one task, we develop a meta-learning framework by training a meta generator to produce perturbations conditioned on benign examples. When attacking a new benign example, the meta generator can be quickly fine-tuned based on the feedback information of the new task as well as a few historical attacks to produce effective perturbations. Moreover, since the meta-train procedure consumes many queries to learn a generalizable generator, we utilize model-level adversarial transferability to train the meta generator on a white-box surrogate model, then transfer it to help the attack against the target model. The proposed framework with the two types of adversarial transferability can be naturally combined with any off-the-shelf query-based attack methods to boost their performance, which is verified by extensive experiments. The source code is available at https://***/SCLBD/MCG-Blackbox. Copyright © 2023, The Authors. All rights reserved.

关键词： Budget control

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Self-Supervised Teaching and Learning of Representations on Graphs 23

Self-Supervised Teaching and Learning of Representations on ...

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32nd ACM World Wide Web Conference, WWW 2023

作者： Wan, Liangtian Fu, Zhenqiang Sun, Lu Wang, Xianpeng Xu, Gang Yan, Xiaoran Xia, Feng Key Laboratory for Ubiquitous Network Service Software of Liaoning Province School of Software Dalian University of Technology Dalian China Department of Communication Engineering Institute of Information Science Technology Dalian Maritime University Dalian China State Key Laboratory of Marine Resource Utilization in South China Sea School of Information and Communication Engineering Hainan University Haikou China State Key Laboratory of Millimeter Waves School of Information Science and Engineering Southeast University Nanjing China Research Center of Big Data Intelligence Research Institute of Artificial Intelligence Zhejiang Lab Hangzhou China School of Computing Technologies Rmit University Melbourne Australia

ISBN: (纸本)9781450394161

Recent years have witnessed significant advances in graph contrastive learning (GCL), while most GCL models use graph neural networks as encoders based on supervised learning. In this work, we propose a novel graph learning model called GraphTL, which explores self-supervised teaching and learning of representations on graphs. One critical objective of GCL is to retain original graph information. For this purpose, we design an encoder based on the idea of unsupervised dimensionality reduction of locally linear embedding (LLE). Specifically, we map one iteration of the LLE to one layer of the network. To guide the encoder to better retain the original graph information, we propose an unbalanced contrastive model consisting of two views, which are the learning view and the teaching view, respectively. Furthermore, we consider the nodes that are identical in muti-views as positive node pairs, and design the node similarity scorer so that the model can select positive samples of a target node. Extensive experiments have been conducted over multiple datasets to evaluate the performance of GraphTL in comparison with baseline models. Results demonstrate that GraphTL can reduce distances between similar nodes while preserving network topological and feature information, yielding better performance in node classification. © 2023 ACM.

关键词： Classification (of information)

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Towards high-throughput and low-latency billion-scale vector search via CPU/GPU collaborative filtering and re-ranking 25

Towards high-throughput and low-latency billion-scale vector...

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Proceedings of the 23rd USENIX Conference on File and Storage Technologies

作者： Bing Tian Haikun Liu Yuhang Tang Shihai Xiao Zhuohui Duan Xiaofei Liao Hai Jin Xuecang Zhang Junhua Zhu Yu Zhang National Engineering Research Center for Big Data Technology and System Service Computing Technology and System Lab/Cluster and Grid Computing Lab School of Computer Science and Technology Huazhong University of Science and Technology China Huawei Technologies Co. Ltd Towards high-throughput and low-latency billion-scale vector search via CPU/GPU collaborative filtering and re-ranking

ISBN: (纸本)9781939133458

Approximate nearest neighbor search (ANNS) has emerged as a crucial component of database and AI infrastructure. Ever-increasing vector datasets pose significant challenges in terms of performance, cost, and accuracy for ANNS services. None of modern ANNS systems can address these issues simultaneously. In this paper, we present Fusion-ANNS, a high-throughput, low-latency, cost-efficient, and high-accuracy ANNS system for billion-scale datasets using SSDs and only one entry-level GPU. The key idea of Fusion-ANNS lies in CPU/GPU collaborative filtering and reranking mechanisms, which significantly reduce I/O operations across CPUs, GPU, and SSDs to break through the I/O performance bottleneck. Specifically, we propose three novel designs: (1) multi-tiered indexing to avoid data swapping between CPUs and GPU, (2) heuristic re-ranking to eliminate unnecessary I/Os and computations while guaranteeing high accuracy, and (3) redundant-aware I/O deduplication to further improve I/O efficiency. We implement FusionANNS and compare it with the state-of-the-art SSD-based ANNS system-SPANN and GPU-accelerated in-memory ANNS system-RUMMY. Experimental results show that FusionANNS achieves 1) 9.4-13.1× higher query per second (QPS) and 5.7-8.8× higher cost efficiency compared with SPANN; 2) and 2-4.9× higher QPS and 2.3-6.8× higher cost efficiency compared with RUMMY, while guaranteeing low latency and high accuracy.

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Iteratively Coupled Multiple Instance Learning from Instance to Bag Classifier for Whole Slide Image Classification

arXiv

引用

arXiv 2023年

作者： Wang, Hongyi Luo, Luyang Wang, Fang Tong, Ruofeng Chen, Yen-Wei Hu, Hongjie Lin, Lanfen Chen, Hao College of Computer Science and Technology Zhejiang University Hangzhou China Department of Computer Science and Engineering The Hong Kong University of Science and Technology Hong Kong Department of Radiology Sir Run Run Shaw Hospital Hangzhou China Research Center for Healthcare Data Science Zhejiang Lab Hangzhou China College of Information Science and Engineering Ritsumeikan University Kusatsu Japan Department of Chemical and Biological Engineering The Hong Kong University of Science and Technology Hong Kong

Whole Slide Image (WSI) classification remains a challenge due to their extremely high resolution and the absence of fine-grained labels. Presently, WSI classification is usually regarded as a Multiple Instance Learning (MIL) problem when only slide-level labels are available. MIL methods involve a patch embedding module and a bag-level classification module, but they are prohibitively expensive to be trained in an end-to-end manner. Therefore, existing methods usually train them separately, or directly skip the training of the embedder. Such schemes hinder the patch embedder’s access to slide-level semantic labels, resulting in inconsistency within the entire MIL pipeline. To overcome this issue, we propose a novel framework called Iteratively Coupled MIL (ICMIL), which bridges the loss back-propagation process from the bag-level classifier to the patch embedder. In ICMIL, we use category information in the bag-level classifier to guide the patch-level fine-tuning of the patch feature extractor. The refined embedder then generates better instance representations for achieving a more accurate bag-level classifier. By coupling the patch embedder and bag classifier at a low cost, our proposed framework enables information exchange between the two modules, benefiting the entire MIL classification model. We tested our framework on two datasets using three different backbones, and our experimental results demonstrate consistent performance improvements over state-of-the-art MIL methods. The code is available at: https://***/Dootmaan/ICMIL. Copyright © 2023, The Authors. All rights reserved.

关键词： Image classification

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