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The Longest Subsequence-Repeated Subsequence Problem 16th

The Longest Subsequence-Repeated Subsequence Problem

引用

16th Annual International Conference on Combinatorial Optimization and Applications, COCOA 2023

作者： Lafond, Manuel Lai, Wenfeng Liyanage, Adiesha Zhu, Binhai Department of Computer Science Université de Sherbrooke QuebecJ1K 2R1 Canada College of Computer Science and Technology Shandong University Qingdao China Gianforte School of Computing Montana State University BozemanMT59717 United States

ISBN: (纸本)9783031496103

Motivated by computing duplication patterns in sequences, a new fundamental problem called the longest subsequence-repeated subsequence (LSRS) is proposed. Given a sequence S of length n, a letter-repeated subsequence is a subsequence of S in the form of x1d1x2d2⋯xkdk with xi a subsequence of S, xj≠ xj+1 and di≥ 2 for all i in [k] and j in [ k- 1 ]. We first present an O(n6) time algorithm to compute the longest cubic subsequences of all the O(n2) substrings of S, improving the trivial O(n7) bound. Then, an O(n6) time algorithm for computing the longest subsequence-repeated subsequence (LSRS) of S is obtained. Finally we focus on two variants of this problem. We first consider the constrained version when Σ is unbounded, each letter appears in S at most d times and all the letters in Σ must appear in the solution. We show that the problem is NP-hard for d= 4, via a reduction from a special version of SAT (which is obtained from 3-COLORING). We then show that when each letter appears in S at most d= 3 times, then the problem is solvable in O(n4) time. © 2024, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Dynamic programming

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Partial Multi-label Learning with a Few Accurately Labeled Data 20th

Partial Multi-label Learning with a Few Accurately Labeled...

引用

20th Pacific Rim International Conference on Artificial Intelligence, PRICAI 2023

作者： Mizuguchi, Haruhi Kimura, Keigo Kudo, Mineichi Sun, Lu Division of Computer Science and Information Technology Graduate School of Information Science and Technology Hokkaido University Sapporo060-0814 Japan School of Information Science and Technology ShanghaiTech University Shanghai201210 China

ISBN: (纸本)9789819970216

Partial Multi-label Learning is a multi-label classification problem where only candidate labels are given for training data. These candidate labels consist of relevant labels and false-positive labels. In this paper, we consider the PML when a few accurately labeled data are available. In practice, it is difficult to remove false-positive labels fully due to a large cost, but it is possible to do that in a few instances with a smaller cost. Conventional PML methods do not assume those accurately labeled data so it is hard to utilize data effectively. We propose a new algorithm called PML-VD to utilize those accurately labeled data. PML-VD first disambiguates the noisy-labeled data with both accurately labeled data and noisy labeled data and then learns a classifier. This two-stage approach enables the effective utilization of accurately labeled data without overfitting. Experiments on nine PML datasets shows the effectiveness of explicit utilization of accurately labeled data. In best cases, PML-VD improves 7% classification accuracy in terms of ranking loss. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd 2024.

关键词： Classification (of information)

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Determination of the Lower Bounds of the Goal Function for a Single-Machine Scheduling Problem on D-Wave Quantum Annealer 1

引用

23rd International Conference on Computational science, ICCS 2023

作者： Bożejko, Wojciech Pempera, Jaroslaw Uchroński, Mariusz Wodecki, Mieczyslaw Department of Control Systems and Mechatronics Wroclaw University of Science and Technology Janiszewskiego 11-17 Wroclaw50-372 Poland Wroclaw Centre for Networking and Supercomputing Wybrzeże Wyspiańskiego 27 Wroclaw50-370 Poland Department of Telecommunications and Teleinformatics Wroclaw University of Science and Technology Wybrzeże Wyspiańskiego 27 Wroclaw50-370 Poland

ISBN: (数字)9783031360305

ISBN: (纸本)9783031360299

The fundamental problem of using metaheuristics and almost all other approximation methods for difficult discrete optimization problems is the lack of knowledge regarding the quality of the obtained solution. In this paper, we propose a methodology for efficiently estimating the quality of such approaches by rapidly – and practically in constant time – generating good lower bounds on the optimal value of the objective function using a quantum machine, which can be an excellent benchmark for comparing approximate algorithms. Another natural application is to use the proposed approach in the construction of exact algorithms based on the Branch and Bound method to obtain real optimal solutions. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023.

关键词： Branch and bound method

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Enhancing Computational science Education Through Practical Applications: Leveraging Predictive Analytics in Box Meal Services 24th

Enhancing Computational Science Education Through Practical ...

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24th International Conference on Computational science (ICCS)

作者： Jacyna-Golda, Ilona Gepner, Pawel Krawiec, Jerzy Halbiniak, Kamil Jankowski, Andrzej Wybraniak-Kujawa, Martyna Warsaw Univ Technol Fac Mech & Ind Engn Warsaw Poland Intel Corp Santa Clara CA USA Czestochowa Tech Univ Fac Mech Engn & Comp Sci Czestochowa Poland

ISBN: (纸本)9783031637858;9783031637834

This paper presents a student project carried out in collaboration with a major industry partner, demonstrating the simultaneous novel application of predictive analytics, in particular machine learning (ML), in the domain of boxed meal services, and explores its implications for IT education. Drawing from a validated ML model trained on data collected from box meal companies, this study showcases how predictive analytics can accurately predict customer sociodemographic characteristics, thereby facilitating targeted marketing strategies and personalized service offerings. By elucidating the methodology and results of the ML model, this article demonstrates the practical utility of computational techniques in real-world electronic services. Moreover, it discusses the pedagogical implications of incorporating such case studies into computational science education, highlighting the opportunities for experiential learning, interdisciplinary collaboration, and industry relevance. Through this exploration, the article contributes to the discourse on innovative teaching methodologies in computational science, emphasizing the importance of bridging theory with practical applications to prepare students for diverse career pathways in the digital era.

关键词： Computational science Education Predictive Analytics Machine Learning Box Meal Services Experiential Learning Interdisciplinary Collaboration Industry Relevance

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Fusing Forces: Deep-Human-Guided Refinement of Segmentation Masks 27th

Fusing Forces: Deep-Human-Guided Refinement of Segmentation ...

引用

27th International Conference on Pattern Recognition, ICPR 2024

作者： Sterzinger, Rafael Stippel, Christian Sablatnig, Robert Computer Vision Lab TU Wien Vienna Austria

ISBN: (纸本)9783031781971

Etruscan mirrors constitute a significant category in Etruscan art, characterized by elaborate figurative illustrations featured on their backside. A laborious and costly aspect of their analysis and documentation is the task of manually tracing these illustrations. In previous work, a methodology has been proposed to automate this process, involving photometric-stereo scanning in combination with deep neural networks. While achieving quantitative performance akin to an expert annotator, some results still lack qualitative precision and, thus, require annotators for inspection and potential correction, maintaining resource intensity. In response, we propose a deep neural network trained to interactively refine existing annotations based on human guidance. Our human-in-the-loop approach streamlines annotation, achieving equal quality with up to 75% less manual input required. Moreover, during the refinement process, the relative improvement of our methodology over pure manual labeling reaches peak values of up to 26%, attaining drastically better quality quicker. By being tailored to the complex task of segmenting intricate lines, specifically distinguishing it from previous methods, our approach offers drastic improvements in efficacy, transferable to a broad spectrum of applications beyond Etruscan mirrors. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Mirrors

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MU-Net: Modified U-Net for Precise Localization and Segmentation of Lumber-Spine Regions from Sagittal Views 27th

MU-Net: Modified U-Net for Precise Localization and Segmenta...

引用

27th International Conference on Pattern Recognition, ICPR 2024

作者： Ahmed, Md. Kaisar Havugimana, Felix Moinudin, Kazi Ashraf Yeasin, Mohammed Department of Electrical and Computer Engineering University of Memphis MemphisTN United States

ISBN: (纸本)9783031781940

Sagittal views provide detailed and critical information about the anatomy and pathology in diagnosing and managing lumbar spine diseases. Radiologists comprehensively evaluate the spinal alignment, structural integrity, and health of bone and soft tissue elements in the sagittal view in diagnosing various spinal conditions and treatment planning. The first step in the diagnosis is localizing the region of interest, typically the lumbar spine segments. To accomplish this, we present a modified U-Net model (MU-Net) for the segmentation and localization of the lumbar spine from sagittal views of Magnetic Resonance Imaging (MRI) images. We employ different techniques to augment data, address the issue of limited training samples, and improve the generalization of deep models. We use the two YOLOv series for localization and MU-Net for segmentation. The MU-Net model achieves an accuracy of 98.93%, a Mean Intersection Over Union (IoU) of 84.29%, and a Dice Coefficient of 98.43%. For the localization, YOLOv8 yields a Precision, Recall, and Mean Average Precision (mAP) of 99.6%, 99.2%, and 99.4%, respectively. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Image segmentation

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P2A: Transforming Proposals to Anomaly Masks 27th

P2A: Transforming Proposals to Anomaly Masks

引用

27th International Conference on Pattern Recognition, ICPR 2024

作者： Zhu, Huachao Sun, Zhichao Liu, Zelong Xu, Yongchao School of Computer Science Wuhan University Wuhan430072 China Hubei Luojia Laboratory Wuhan430079 China

ISBN: (纸本)9783031801358

Detecting anomalies in road scenes is essential for safe autonomous driving. Existing methods often consider the likelihood of pixels not belonging to a closed set of classes as the anomaly score. However, this approach lacks object-level understanding and frequently results in numerous false positives at boundaries and ambiguous regions. In this paper, we present a novel method that directly computes the probability of pixels being anomalous and outputs both anomaly segmentation results and score maps. Our approach utilizes the rich semantic information correlated to linguistic concepts in Stable Diffusion to compensate for the low coverage of anomalies caused by limited annotated samples. Using a query-based segmentation model, we transform the proposals into masks of both in-distribution and out-of-distribution objects. Additionally, we introduce an image-mask-image pipeline to generate various annotated data as outliers for supervised training. Extensive experiments across multiple benchmarks confirm that the proposed method outperforms previous state-of-the-art methods in road anomaly segmentation. Code is available at https://***/huachao0124/P2A. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Semantic Segmentation

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An Ontology-Based Approach for Anomaly Detection in Business Processes 25th

An Ontology-Based Approach for Anomaly Detection in Business...

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25th International Conference on Web Information Systems Engineering, WISE 2024

作者： Musa, Tahani H. Abu Bouras, Abdelaziz Belhi, Abdelhak Information Technology Department Community College of Qatar Doha Qatar Department of Computer Science and Engineering Qatar University Doha Qatar Joaan BinJassim Academy for Defense Studies Doha Qatar

ISBN: (纸本)9789819605781

Business Process Mining is an emerging field dedicated to the analysis of Business Process Models (BPM) by deriving insights from transaction logs generated through various industrial and Supply Chain Management execution information systems, such as a ERP or an MES. This approach facilitates the examination, assessment, and improvement of business activities across the entire lifecycle, from initiation to completion. In this paper, we propose a framework to detect anomalies in business processes by analyzing event logs produced from purchasing transactions. The framework focuses on anomaly detection in the supply chain and aims to improve its accuracy, boost its workflow across multiple business process levels, and improve supply chain processes with an emphasis on security and automation. The suggested approach uses Ontology and designed SWRL rules to classify anomalies in business transactions. The framework has been evaluated using transaction logs from a simulated general a purchasing scenario, as an example of a business process mode, proving its capability to effectively identify and categorize anomalous business transactions. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Ontology

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DD-Net: Dynamic Network Architecture for Optimized Curve Segmentation and Reduce Computational Redundancy 27th

DD-Net: Dynamic Network Architecture for Optimized Curve Seg...

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27th International Conference on Pattern Recognition, ICPR 2024

作者： Cao, Yunxiang Chen, Li Wang, Yubo Feng, Zhida Tian, Jing School of Computer Science and Technology Wuhan University of Science and Technology Wuhan China Hubei Province Key Laboratory of Intelligent Information Processing and Real-time Industrial System Wuhan University of Science and Technology Wuhan China Institute of Systems Science National University of Singapore Singapore Singapore

ISBN: (纸本)9783031783821

Curvilinear Structure segmentation has numerous applications in various fields, including providing a better understanding of defects such as cracks on roads or walls, thereby assessing the structural safety of buildings. Numerous curve segmentation methods based on Convolutional Neural Networks(CNN) have been developed in recent years. However, preserving feature integrity remains a challenge. To address this issue, this paper introduces a double dynamic network, called DD-Net, which consists of a dynamic structure for training and dynamic masking for inference. Firstly, to enhance the nonlinearity of the model, a paradigm to auto-adjust the CNN structure via a trainable module is formed for better fine-grained feature extraction. This dynamic structure (DS) paradigm enables a trade-off to extract or discard the feature data in the model training. Due to the data heterogeneity between the training and inference data, models may contain some useless nodes that are less effective during inference. The dynamic masking (DM) is proposed to omit the useless nodes based on the score difference between the train and inference feature statistics, thereby reducing redundant computations. To further improve the model’s performance on thin-curve segmentation and to preserve feature integrity, we introduce a non-curve suppression (NCS) module. This module focuses on background information while considering foreground prediction to address noisy conditions. The experimental results show that our DD-Net achieves promising results on three benchmark datasets and outperforms state-of-the-art curve segmentation models. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Convolutional neural networks

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Zero-shot Automated Class Imbalanced Learning 27th

Zero-shot Automated Class Imbalanced Learning

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27th International Conference on Pattern Recognition, ICPR 2024

作者： Wang, Zhaoyang Wang, Shuo School of Computer Science University of Birmingham BirminghamB15 2TT United Kingdom

ISBN: (纸本)9783031783821

Given a new class imbalanced dataset D and limited computational resources, the challenge arises of selecting promising class imbalanced learning (CIL) pipelines that include resampling methods, classification models, and their corresponding hyperparameters. To address this challenge, we study Zero-shot Automated Machine Learning and propose a new approach aiming at class imbalanced data, called Zero-shot Automated Class Imbalance Learning (ZAutoCIL). ZAutoCIL employs domain-independent meta-learning to develop a zero-shot surrogate model for automated class imbalanced learning. This model aims to recommend effective CIL pipelines for new unseen imbalanced datasets without requiring additional search. Specifically, we meta-train a two-tower model to serve as the surrogate model, adapted from recommender systems, using a pairwise ranking loss on the meta-dataset gained from collecting performance data across a wide range of CIL pipelines and a comprehensive repository of class imbalance datasets. We perform extensive experiments on 100 datasets grouped in 4 parts based on their imbalance ratio. The experimental results demonstrate the efficacy of our approach in automating the recommendation of CIL pipelines given any target imbalanced datasets. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Zero-shot learning

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