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arXiv 2024年

作者： Pham, Hoang Giang Dam, Tien Thanh Duong, Ngan Ha Mai, Tien Hà, Minh Hoàng ORLab Faculty of Computer Science Phenikaa University Ha Dong Hanoi Viet Nam ORLab-SLSCM Faculty of Data Science and Artificial Intelligence National Economics University Hai Ba Trung Hanoi Viet Nam School of Computing and Information Systems Singapore Management University 80 Stamford Rd 178902 Singapore Department of Engineering Computer Science and Mathematics University of L'Aquila AQ L'Aquila67100 Italy

In this paper, we study a facility location problem within a competitive market context, where customer demand is predicted by a random utility choice model. Unlike prior research, which primarily focuses on simple constraints such as a cardinality constraint on the number of selected locations, we introduce routing constraints that necessitate the selection of locations in a manner that guarantees the existence of a tour visiting all chosen locations while adhering to a specified tour length upper bound. Such routing constraints find crucial applications in various real-world scenarios. The problem at hand features a non-linear objective function, resulting from the utilization of random utilities, together with complex routing constraints, making it computationally challenging. To tackle this problem, we explore three types of valid cuts, namely, outer-approximation and submodular cuts to handle the nonlinear objective function, as well as sub-tour elimination cuts to address the complex routing constraints. These lead to the development of two exact solution methods: a nested cutting plane and nested branch-and-cut algorithms, where these valid cuts are iteratively added to a master problem through two nested loops. We also prove that our nested cutting plane method always converges to optimality after a finite number of iterations. Furthermore, we develop a local search-based metaheuristic tailored for solving large-scale instances and show its pros and cons compared to exact methods. Extensive experiments are conducted on problem instances of varying sizes, demonstrating that our approach excels in terms of solution quality and computation time when compared to other baseline approaches. Copyright © 2024, The Authors. All rights reserved.

关键词： Location

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On the -differential spectra of two classes of power functions over finite fields

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Applicable Algebra in Engineering, Communication and computing 2025年 1-30页

作者： Liu, Qian Huang, Zhiwei Chen, Zhixiong Jiang, Rong Zhang, Liupiao Key Laboratory of Information Security of Network Systems College of Computer and Data Science Fuzhou University Fuzhou China Hubei Key Laboratory of Applied Mathematics Faculty of Mathematics and Statistics Hubei University Wuhan China Yunnan Key Laboratory of Service Computing Yunnan University of Finance and Economics Kunming China Fujian Key Laboratory of Financial Information Processing Putian University Putian China School of Computer Science and Technology Dongguan University of Technology Dongguan China

Recently, a new concept called multiplicative differential was introduced by Ellingsen et al. [7]. As an extension of the differential uniformity, it is theoretically appealing to determine the properties of c-differential uniformity and the corresponding c-differential spectrum. In this paper, based on certain quadratic character sums and two special elliptic curves over $$\mathbb {F}_p$$ , the $$(-1)$$ -differential spectra of the following two classes of power functions over $$\mathbb {F}_{p^n}$$ is completely determined: (1) $$f_1(x)=x^{\frac{p^n+3}{2}}$$ , where $$p>3$$ and $$p\equiv 3\pmod 4$$ ; (2) $$f_2(x)=x^{p^n-3}$$ , where $$p>3$$ . The obtained result shows that the $$(-1)$$ -differential spectra of $$f_1(x)$$ and $$f_2(x)$$ can be expressed explicitly in terms of n. Moreover, an upper bound of the c-differential uniformity of $$f_2(x)$$ is given.

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Embarrassingly Simple Scribble Supervision for 3D Medical Segmentation

arXiv

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arXiv 2024年

作者： Gotkowski, Karol Lüth, Carsten Jäger, Paul F. Ziegler, Sebastian Krämer, Lars Denner, Stefan Xiao, Shuhan Disch, Nico Maier-Hein, Klaus H. Isensee, Fabian Division of Medical Image Computing DKFZ Heidelberg Germany Helmholtz Imaging DKFZ Heidelberg Germany Interactive Machine Learning Group DKFZ Heidelberg Germany Medical Faculty Heidelberg University of Heidelberg Heidelberg Germany Faculty of Mathematics and Computer Science Heidelberg University Germany HIDSS4Health - Helmholtz Information and Data Science School for Health Germany

Traditionally, segmentation algorithms require dense annotations for training, demanding significant annotation efforts, particularly within the 3D medical imaging field. Scribble-supervised learning emerges as a possible solution to this challenge, promising a reduction in annotation efforts when creating large-scale datasets. Recently, a plethora of methods for optimized learning from scribbles have been proposed, but have so far failed to position scribble annotation as a beneficial alternative. We relate this shortcoming to two major issues: 1) the complex nature of many methods which deeply ties them to the underlying segmentation model, thus preventing a migration to more powerful state-of-the-art models as the field progresses and 2) the lack of a systematic evaluation to validate consistent performance across the broader medical domain, resulting in a lack of trust when applying these methods to new segmentation problems. To address these issues, we propose a comprehensive scribble supervision benchmark consisting of seven datasets covering a diverse set of anatomies and pathologies imaged with varying modalities. We furthermore propose the systematic use of partial losses, i.e. losses that are only computed on annotated voxels. Contrary to most existing methods, these losses can be seamlessly integrated into state-of-the-art segmentation methods, enabling them to learn from scribble annotations while preserving their original loss formulations. Our evaluation using nnU-Net reveals that while most existing methods suffer from a lack of generalization, the proposed approach consistently delivers state-of-the-art performance. Thanks to its simplicity, our approach presents an embarrassingly simple yet effective solution to the challenges of scribble supervision. Source code as well as our extensive scribble benchmarking suite will be made publicly available upon publication. © 2024, CC BY.

关键词： Medical imaging

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Investigating effective transfer of deep learning models from adults to children for lung ultrasound data analysis

Investigating effective transfer of deep learning models fro...

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2023 IEEE International Ultrasonics Symposium, IUS 2023

作者： Thompson, Russell Khan, Umair Li, Jason Etter, Lauren. P. Camello, Ingrid Pieciak, Rachel. C. Castro-Aragon, Ilse. Setty, Bindu Gill, Christopher. C. Demi, Libertario Betke, Margrit University of Massachusetts DartmouthMA United States University of Trento Department of Information Engineering & Computer Science TN 38123 Italy Boston University The Faculty of Computing & Data Sciences BostonMA United States University of Wisconsin Madison MadisonWI United States Augusta University Pediatric Infectious Disease AugustaGA United States Boston University School of Public Health Department of Global Health BostonMA United States Boston Medical Center Department of Radiology BostonMA United States

ISBN: (纸本)9798350346459

Automated assessment of LUS data from adult population is an ongoing research over the last few years. To this extent, various deep learning DL-based methods have been proposed to assess lung alterations caused by pneumonia. However, no work has been done in this regard to evaluate the LUS data from children for the presence/absence of consolidations due to pneumonia. Pediatric healthcare data acquisition in a resource constraint environment is a challenging task. As a result of which limited data is acquired. This prevents the effective training of DL models for the development of reliable computer aided assessment systems. To address this challenge, we employed pre-trained DL models, previously trained on LUS data from adults, and evaluated them over LUS data from children in a transfer learning (TL) setting. Two TL strategies i.e. linear probing (LP) and fine-tuning (FT) are utilized. The models are trained and tested in a leave-one-out cross validation manner with results showing that all the pre-trained models achieved higher classification performance for FT in comparison to LP. Among the models it is found that with the addition of spatial attention and custom loss to the base CNN, the model outperformed with the highest mean F1-Score of 81.9%. While evaluating the model's performance w.r.t age, it is found that the more accurate the model, the less impact of age has on the underlying classification performance. These findings lay down the foundation for accurate consolidation classification in LUS data from children in a resource constraint environment © 2023 IEEE.

关键词： Ultrasonics

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A Triangular Stable Node Network based on Self-supervised Learning for personalized prediction

A Triangular Stable Node Network based on Self-supervised Le...

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2025 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2025

作者： Liu, Qing Gao, Qian Fan, Jun Zhang, Zhiqiang Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center National Supercomputer Center in Jinan Qilu University of Technology Shandong Academy of Sciences Jinan250014 China Shandong Engineering Research Center of Big Data Applied Technology Faculty of Computer Science and Technology Qilu University of Technology Shandong Academy of Sciences Jinan250353 China Shandong Provincial Key Laboratory of Industrial Network and Information System Security Shandong Fundamental Research Center for Computer Science Jinan250014 China China Telecom Digital Intelligence Techonology Co Ltd. No.1999 Shunhua road Shandong Jinan250101 China

ISBN: (纸本)9798350368741

In recent years, research has illuminated the potency of implicit data processing in enhancing user preferences. Nevertheless, barriers remain in breaking through the constraints of implicit information. This study aims to bridge this gap by firstly constructing a triangular stable node network model, tailored to manage implicit information with precision. Recognizing the challenge of pinpointing novel structures within large-scale graphs, we propose SLTSNN-a triangular stable node network based on self-supervised learning for personalized prediction. SLTSNN innovates by maximizing mutual information between graph-level and patch-level representations, while augmenting graph representations through extended enhanced representations. Additionally, it incorporates triangular data associations and introduces a triangular allocation attention network, which emphasizes strongly correlated preference features among similar users. Furthermore, SLTSNN employs contrastive learning to maximize mutual information between graph vectors and hidden representations, distinguishing between high-order global and local representations. The model's effectiveness in enhancing user preferences and capturing new graph structures is evidenced by its performance on hit rate and normalized discounted cumulative gain metrics across three datasets. © 2025 IEEE.

关键词： graph contrastive learning graph neural networks recommender systems self-supervised learning

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Beamforming Design for Physical Security in Movable Antenna-aided ISAC Systems: A Reinforcement Learning Approach

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IEEE Transactions on Vehicular Technology 2025年

作者： Hung, Hoang Le Huy, Nguyen Hoang Luong, Nguyen Cong Pham, Quoc-Viet Niyato, Dusit Hoa, Nguyen Tien Phenikaa University Faculty of Computer Science Hanoi 12116 Viet Nam Hanoi University of Science and Technology School of Electrical and Electronic Engineering Hanoi 100000 Viet Nam Trinity College Dublin School of Computer Science and Statistics D02 PN40 Ireland Nanyang Technological University College of Computing and Data Science Singapore

In this paper, we investigate a secure Integrated Sensing and Communication (ISAC) system aided by Movable Antennas (MAs). Therein, the positions of the MAs can be dynamically adjusted to enhance channel conditions, thereby improving communication security for legitimate users in the presence of adversaries. Additionally, we consider a realistic scenario, where the Base Station (BS) only has imperfect eavesdropper's Channel State Information (CSI). In this context, our objective is to optimize the system secrecy rate by jointly designing the BS's transmit beamforming vector and MA movement. To address the dynamics of the ISAC environments and uncertainties of the imperfect CSI scenario, as well as the complexity and non-convexity of the problem, we propose a deep reinforcement learning method to simultaneously design transmit beamforming and MA movement to against eavesdropper. The simulation results show that the use of MAs improves the secrecy rate by 40% and the proposed algorithm outperforms baseline methods in terms of adaptability and performance. © 2025 IEEE.

关键词： Integrated Sensing and Communication Movable Antenna Physical Layer Security Reinforcement Learning

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A Deep Learning Approach Towards Student Performance Prediction in Online Courses: Challenges Based on a Global Perspective

arXiv

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arXiv 2024年

作者： Moubayed, Abdallah Injadat, MohammadNoor Alhindawi, Nouh Samara, Ghassan Abuasal, Sara Alazaidah, Raed School of Computing and Augmented Intelligence Arizona State University GilbertAZ United States Electrical and Computer Engineering Department Lebanese American University Byblos Lebanon Data Science and Artificial Intelligence Department Faculty of Information Technology Zarqa University Zarqa Jordan Electrical and Computer Engineering Department Western University LondonON Canada Faculty of Science and Information Technology Jadara University Irbid Jordan Computer Science Department Faculty of Information Technology Zarqa University Zarqa Jordan Software Engineering Department Faculty of Information Technology Zarqa University Zarqa Jordan

Analyzing and evaluating students’ progress in any learning environment is stressful and time consuming if done using traditional analysis methods. This is further exasperated by the increasing number of students due to the shift of focus toward integrating the Internet technologies in education and the focus of academic institutions on moving toward e-Learning, blended, or online learning models. As a result, the topic of student performance prediction has become a vibrant research area in recent years. To address this, machine learning and data mining techniques have emerged as a viable solution. To that end, this work proposes the use of deep learning techniques (CNN and RNN-LSTM) to predict the students’ performance at the midpoint stage of the online course delivery using three distinct datasets collected from three different regions of the world. Experimental results show that deep learning models have promising performance as they outperform other optimized traditional ML models in two of the three considered datasets while also having comparable performance for the third dataset. Copyright © 2024, The Authors. All rights reserved.

关键词： Students

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Machine Learning and OpenCV: A Powerful Combination for Driver Drowsiness Detection

Machine Learning and OpenCV: A Powerful Combination for Driv...

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International Conference on Communication, computing and Internet of Things (IC3IoT)

作者： A Siva Kumar Pv Gopirajan Jayaraj Ramasamy Beulah Jackson Department of Data Science and Business Systems School of Computing SRM Institute of Science and Technology Kattankulathur Chennai India Department of Computational Intelligence School of Computing SRM Institute of Science and Technology Kattankulathur Campus Chennai India Department of IT Faculty of Engineering and Technology Botho University Botswana-Africa Department of Electronics and Communication Engineering Vel Tech Rangarajan Dr. Sagunthala R & D Institute of Science and Technology Chennai India

ISBN: (数字)9798350352689

ISBN: (纸本)9798350352696

Driver fatigue is a significant contributor to road accidents worldwide, and there is a need for efficient driver drowsiness detection systems to prevent such accidents. Computer vision techniques like OpenCV have recently been utilized to develop such strategies. This proposed work investigates the use of machine learning models and OpenCV Python package for driver drowsiness detection. The proposed system uses a camera to continuously capture the driver’s face and applies image processing techniques to identify drowsiness. The system detects blinks by monitoring the driver’s eyes and measuring the frequency and duration of eyelid closures. The real-time experiments conducted with the proposed method showed high accuracy in detecting drowsiness, and the system can be implemented in various settings, including cars and heavy vehicles. Ultimately, the system has some potential to reduce accidents caused by driver fatigue, thereby enhancing road safety.

关键词： Support vector machines Face recognition Machine learning Feature extraction Fatigue Real-time systems Vectors

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Touchstone benchmark: are we on the right way for evaluating AI algorithms for medical segmentation? 24

Touchstone benchmark: are we on the right way for evaluating...

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

作者： Pedro R. A. S. Bassi Wenxuan Li Yucheng Tang Fabian Isensee Zifu Wang Jieneng Chen Yu-Cheng Chou Saikat Roy Yannick Kirchhoff Maximilian Rokuss Ziyan Huang Jin Ye Junjun He Tassilo Wald Constantin Ulrich Michael Baumgartner Klaus H. Maier-Hein Paul Jaeger Yiwen Ye Yutong Xie Jianpeng Zhang Ziyang Chen Yong Xia Zhaohu Xing Lei Zhu Yousef Sadegheih Afshin Bozorgpour Pratibha Kumari Reza Azad Dorit Merhof Pengcheng Shi Ting Ma Yuxin Du Fan Bai Tiejun Huang Bo Zhao Haonan Wang Xiaomeng Li Hanxue Gu Haoyu Dong Jichen Yang Maciej A. Mazurowski Saumya Gupta Linshan Wu Jiaxin Zhuang Hao Chen Holger Roth Daguang Xu Matthew B. Blaschko Sergio Decherchi Andrea Cavalli Alan L. Yuille Zongwei Zhou Department of Computer Science Johns Hopkins University and Department of Pharmacy and Biotechnology University of Bologna and Center for Biomolecular Nanotechnologies Istituto Italiano di Tecnologia Department of Computer Science Johns Hopkins University NVIDIA Division of Medical Image Computing German Cancer Research Center (DKFZ) and Helmholtz Imaging German Cancer Research Center (DKFZ) ESAT-PSI KU Leuven Division of Medical Image Computing German Cancer Research Center (DKFZ) and Faculty of Mathematics and Computer Science Heidelberg University Division of Medical Image Computing German Cancer Research Center (DKFZ) and Faculty of Mathematics and Computer Science Heidelberg University and HIDSS4Health - Helmholtz Information and Data Science School for Health Shanghai Jiao Tong University Shanghai Artificial Intelligence Laboratory Division of Medical Image Computing German Cancer Research Center (DKFZ) Division of Medical Image Computing German Cancer Research Center (DKFZ) and Pattern Analysis and Learning Group Department of Radiation Oncology Heidelberg University Hospital Helmholtz Imaging German Cancer Research Center (DKFZ) and Interactive Machine Learning Group (IML) DKFZ School of Computer Science and Engineering Northwestern Polytechnical University Australian Institute for Machine Learning The University of Adelaide College of Computer Science and Technology Zhejiang University Hong Kong University of Science and Technology (Guangzhou) Hong Kong University of Science and Technology (Guangzhou) and Hong Kong University of Science and Technology Faculty of Informatics and Data Science University of Regensburg Faculty of Electrical Engineering and Information Technology RWTH Aachen University Faculty of Informatics and Data Science University of Regensburg and Fraunhofer Institute for Digital Medicine MEVIS Electronic & Information Engineering School Harbin Institute of Technology (Shenzhen) Shanghai Jiao Tong University and Beijing Academy of Artificial Intelligence (BAAI) S

ISBN: (纸本)9798331314385

How can we test AI performance? This question seems trivial, but it isn't. Standard benchmarks often have problems such as in-distribution and small-size test sets, oversimplified metrics, unfair comparisons, and short-term outcome pressure. As a consequence, good performance on standard benchmarks does not guarantee success in real-world scenarios. To address these problems, we present Touchstone, a large-scale collaborative segmentation benchmark of 9 types of abdominal organs. This benchmark is based on 5,195 training CT scans from 76 hospitals around the world and 5,903 testing CT scans from 11 additional hospitals. This diverse test set enhances the statistical significance of benchmark results and rigorously evaluates AI algorithms across out-of-distribution scenarios. We invited 14 inventors of 19 AI algorithms to train their algorithms, while our team, as a third party, independently evaluated these algorithms. In addition, we also evaluated pre-existing AI frameworks—which, differing from algorithms, are more flexible and can support different algorithms—including MONAI from NVIDIA, nnU-Net from DKFZ, and numerous other open-source frameworks. We are committed to expanding this benchmark to encourage more innovation of AI algorithms for the medical domain.

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Enhancing Spam Email Classification using Multilayer Perceptron: Performance Analysis and Comparative Evaluation

Enhancing Spam Email Classification using Multilayer Percept...

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International Conference on Pattern Analysis and Intelligent Systems (PAIS)

作者： Ghizlane Khababa Nor Hazlyna Harun Sadik Bessou Chu Sin Ning Ooi Poa Ning Department of Computer Sciences Faculty of Sciences Sétif 1 University Sétif Algeria Data Science Research Lab (DSRL) School of Computing Universiti Utara Malaysia Sintok Kedah Malaysia School of Computing Universiti Utara Malaysia Sintok Kedah Malaysia

With the rapid increase in spam email volume and the associated productivity and security risks, effective spam email classification mechanisms are crucial in ensuring smooth communication systems. After several studies on spam email classification, the outcome demonstrated that Multilayer Perceptron (MLP) outperforms SVM and Decision Trees, making it the chosen model for further research in this paper, as it presented a study on spam email classification using a multilayer perceptron (MLP) model. The proposed MLP-based approach demonstrates strong performance in distinguishing between legitimate and spam emails. A comprehensive evaluation is conducted using a dataset of 5,172 emails, resulting in an accuracy rate of 94.40% on the testing set. The analysis of the results reveals interesting patterns and trends in the data, providing valuable insights into the effectiveness of the MLP model. Furthermore, a comparison with some state-of-the-art approaches showcases the superiority of the MLP model in terms of accuracy, F1 score, precision, recall, and MSE. The results of this study aid in the creation of reliable and precise spam email classification systems, increasing productivity and defending communication channels against spam threats.

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