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11th International Symposium on Integrated Uncertainty in Knowledge Modelling and Decision Making, IUKM 2025

作者： Burimas, Kanokwan Gorka, Artur Thipwiwatpotjana, Phantipa Department of Mathematics and Computer Science Faculty of Science Chulalongkorn University Bangkok10330 Thailand Department of Mathematics Erskine College Due WestSC29639 United States

ISBN: (纸本)9789819646029

Traditional methods for solving interval linear programming problems often involve transforming the problem into two sub-problems to determine the interval objective value. However, these methods generally apply only when the best and the worst sub-problems are bounded. This work presents a solution to the problem when the best sub-problem is unbounded and the worst sub-problem is bounded. In a situation when interval parameters in the constraints could be changed, we apply a perturbation method to convert the unbounded best sub-problems into a bounded one, based on vector projection and with the minimal changes of the objective coefficient. This method also covers solutions when the new coefficients fall outside of the original range. Numerical examples are provided to demonstrate the method. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Integer linear programming

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AfriSign: African sign languages machine translation

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Discover Artificial Intelligence 2025年第1期5卷 1-16页

作者： Takyi, Kate Gyening, Rose-Mary Owusuaa Mensah Gueuwou, Shester Msouobu Nyarko, Marco Stanley Adade, Richard Borkor, Reindorf Nartey Boadu-Acheampong, Samuelson Israel Tabari, Linus Department of Computer Science Kwame Nkrumah University of Science and Technology Kumasi Ghana Department of Disability and Rehabilitation Studies Kwame Nkrumah University of Science and Technology Kumasi Ghana Department of Mathematics Kwame Nkrumah University of Science and Technology Kumasi Ghana

Research on sign language translation is ongoing with a high social inclusive goal of crossing the bridge between people with hearing disability using sign language as their basic way to communicate to others who do not understand sign language. Hundreds of different sign languages exist instead of a single universal sign language. Research on translating sign languages from high-income nations has grown significantly, but little is known about translating sign languages from Africa. In this paper, we curate a novel video-to-text African sign languages translation dataset containing sign language videos of Bible verses from six (6) different African countries. We experimented with competitive machine translation and sign language translation techniques on our dataset, including the application of transformers to sign language translation, multilingual training, and cross-transfer learning. We evaluated them in terms of accuracy and precision. The results from our experiments prove that having one Multilingual model for all the languages tends to be a better choice when deployed in real system in terms of memory usage with an accuracy of 94.6% and precision of 97.3%. These results give headway for more multilingual models to be developed to enhance inclusion for the deaf community and bridge the gap between the hearing and the deaf in Africa. © The Author(s) 2025.

关键词： Machine translation

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A General Framework for Clustering and Distribution Matching with Bandit Feedback

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IEEE Transactions on Information Theory 2025年第3期71卷 2116-2139页

作者： Yavas, Recep Can Huang, Yuqi Tan, Vincent Y. F. Scarlett, Jonathan CNRS@CREATE DesCartes Program CREATE Tower 138602 Singapore National University of Singapore Department of Computer Science Queenstown119077 Singapore National University of Singapore School of Computing Department of Information Systems and Analytics Queenstown119077 Singapore National University of Singapore Department of Mathematics Department of Electrical and Computer Engineering Queenstown119077 Singapore Institute of Data Science National University of Singapore Department of Computer Science The Department of Mathematics Queenstown119077 Singapore

We develop a general framework for clustering and distribution matching problems with bandit feedback. We consider a K-armed bandit model where some subset of K arms is partitioned into M groups. Within each group, the random variable associated to each arm follows the same distribution on a finite alphabet. At each time step, the decision maker pulls an arm and observes its outcome from the random variable associated to that arm. Subsequent arm pulls depend on the history of arm pulls and their outcomes. The decision maker has no knowledge of the distributions of the arms or the underlying partitions. The task is to devise an online algorithm to learn the underlying partition of arms with the least number of arm pulls on average and with an error probability not exceeding a pre-determined value δ. Several existing problems fall under our general framework, including finding M pairs of arms, odd arm identification, and N-ary clustering of K arms belong to our general framework. We derive a non-asymptotic lower bound on the average number of arm pulls for any online algorithm with an error probability not exceeding δ. Furthermore, we develop a computationally-efficient online algorithm based on the Track-and-Stop method and Frank-Wolfe algorithm, and show that the average number of arm pulls of our algorithm asymptotically matches that of the lower bound. Our refined analysis also uncovers a novel bound on the speed at which the average number of arm pulls of our algorithm converges to the fundamental limit as δ vanishes. © 1963-2012 IEEE.

关键词： Random variables

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Knowledge Discovery of the Internet of Things (IoT) Using Large Language Model

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International Journal of Advanced computer science and Applications 2025年第4期16卷 993-998页

作者： Alsulami, Bassma Saleh Department of Computer Science Faculty of Computing and Information Technology King Abdulaziz University Jeddah21589 Saudi Arabia

Internet of Things (IoT) technology quickly transformed traditional management and engagement techniques in several sectors. This work explores the trends and applications of the Internet of Things in industries, including agriculture, education, transportation, water management, air quality monitoring, underground mining, smart retail, smart home systems, and weather forecasting. The methodology involves a comprehensive review of the literature, followed by data extraction and analysis using BERT to identify key insights and patterns in IoT applications. The findings show that IoT significantly impacts the improvement of real-time monitoring, increasing efficiency, and encouraging innovative solutions in various sectors. Despite its transformative potential, cybersecurity threats, data privacy concerns, and the need for strong policy frameworks persist. The study emphasizes the necessity of multidisciplinary approaches to address these difficulties and optimize IoT implementation. Future research should focus on establishing secure IoT systems, maintaining data integrity, and encouraging collaboration between disciplines to realise the benefits of IoT technology. © (2025), (science and Information Organization). All rights reserved.

关键词： Data integrity

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Leader-follower green traffic assignment problem with online supervised machine learning solution approach

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Soft Computing 2025年第1期29卷 103-116页

作者： Sadra, M. Zaferanieh, M. Yazdimoghaddam, J. Department of Mathematics and Computer Science Hakim Sabzevari University Sabzevar Iran Department of Industrial Engineering Hakim Sabzevari University Sabzevar Iran

In this paper, we propose a bi-level green traffic assignment with network design problem. At the upper level, the objective function evaluates a total traffic Carbon monoxide (CO) gas emissions problem to provide a macroscopic viewpoint of the system manager. At the lower-level, a traffic assignment network design problem is considered to individually optimize users’ travel times, with certain links being potential candidates for network addition. Although the lower-level objective function is convex with linear constraints, the proposed bi-level problem is np-hard, and even finding a near-optimal solution is an np-hard task. To address the solution approach, we applied an online supervised machine learning (SML) algorithm which solves the proposed bi-level problem within a reasonable running time. Additionally, a mat-heuristic algorithm is proposed to compare the results with the online SML algorithm. To validate the online SML algorithm, we conducted experiments using real urban transportation examples in medium and large-sized networks. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2025.

关键词： Travel time

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STUDY OF THE LINEAR AND NONLINEAR REGIMES OF NATURAL CONVECTION WITH WEAK OR DOMINATING INTERNAL HEAT GENERATION FOR RIGID-FREE BOUNDARIES

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Journal of Porous Media 2025年第9期28卷 21-39页

作者： Kruthik, P.S. Idris, Ruwaidiah Siddheshwar, P.G. Faculty of Computer Science and Mathematics Universiti Malaysia Terengganu Kuala Nerus Terengganu 21030 Malaysia Centre for Mathematical Needs Department of Mathematics CHRIST University Bengaluru560029 India

The paper presents the linear and non-linear regimes of natural convection in the presence of uniform internal heat generation for rigid-free boundaries. A linear stability analysis followed by nonlinear stability analysis is carried out for using a novel procedure. The eigenvalue of the two problems are different. The first one has Rayleigh number based on internal heat generation as the eigenvalue while the second, which is of the classical Bénard type, has a buoyancyRayleigh number. The critical Rayleigh number in both problems is initially determined using the single-term Galerkin method, followed by a refinement of the value by the Maclaurin series method. The findings indicate that the system becomes stable with increasing values of the porous parameter and the Brinkman number. The percentage relative error in the eigenvalue obtained by the single-term Galerkin method relative to that obtained by the Maclaurin series method is presented. In the second natural convection problem, we have two Rayleigh numbers, viz., the weak internal Rayleigh number, RI, and the external Rayleigh number, Ra. The effect of RI on Rac is to reduce it in the case of a heat source and increase it in the case a of heat sink. Additionally, conditions facilitating the transition from Brinkman– Bénard convection to Darcy–Bénard convection are presented. The Ginzburg–Landau equation is obtained for both the problems and the scaled Lorenz model is derived in the case of second problem. The solution from the Ginzburg–Landau equation is used to plot for the amplitude and results are illustrated. © 2025 by Begell House,.

关键词： Linear stability analysis

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Light Weighted YOLO Vehicle Tracker for Toll Gate Using Centroid Algorithm 3rd

Light Weighted YOLO Vehicle Tracker for Toll Gate Using Cen...

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3rd International Conference on Emerging Electronics and Automation, E2A 2023

作者： Singh, Pankaj Pratap Prasad, Shitala Kalita, Gyanjyoti Das, Jahnu Mahanta, Pallab Jyoti Department of Computer Science and Engineering CIT Kokrajhar Assam India School of Mathematics and Computer Science IIT Goa Goa India

ISBN: (纸本)9789819730896

This paper focuses on vehicle detection at highway toll gates. The study involves the detection of vehicles within a custom dataset, which comprises four vehicle classes: car, truck, bus, and motorcycle. The YOLOv4 model is utilized for training and is complemented by the implementation of the Centroid tracking algorithm. In the proposed vehicle track dataset, each class comprises 1500 annotated images for training and 300 images in the testset. These images are sourced from the Google Open Images V6 dataset, and the OIDv4 toolkit is employed to scrape dataset images. The primary objective of this study is to train a vehicle detection model and evaluate the effectiveness of various tracking algorithms, with a specific focus on the centroid tracker for real-time application at the toll gates. Importantly, this model is designed to operate efficiently on conventional and cost-effective GPUs and demonstrates a commendable level of precision. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Image annotation

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Deep Learning and Federated Learning in Breast Cancer Screening and Diagnosis: A Systematic Review

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IEEE Access 2025年 13卷 76322-76351页

作者： Ciobotaru, Alexandru Corches, Cosmina Gota, Dan Miclea, Liviu Technical University of Cluj-Napoca Faculty of Automation and Computer Science Automation Department Cluj-Napoca400114 Romania

Breast cancer remains one of the leading causes of death among women worldwide. Early detection and diagnosis are crucial for effective treatment and improved patient outcome. In recent years, both deep learning and federated learning have demonstrated significant advancements in terms of their ability to reliably and securely diagnose breast cancer using various imaging techniques. Consequently, a thorough summary of recent advancements in the field of breast cancer screening and diagnosis using deep learning and federated learning, along with future research directions, is imperative. This study presents a comprehensive review of recent deep learning and federated learning-based methods for breast cancer screening and diagnosis from the classification, segmentation, and detection viewpoints using ultrasonography, mammography, magnetic resonance imaging, and histopathology images. In addition, novel deep learning and federated learning architectures that are used to reliably and securely diagnose breast cancer using various imaging techniques are highlighted. Finally, a discussion of the associated challenges and future research directions are presented. © 2013 IEEE.

关键词： Deep reinforcement learning

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Privacy-Aware Secure Data Auditing for Cloud-based Intelligence of Things Environment

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IEEE Internet of Things Journal 2025年第11期12卷 15288-15303页

作者： Ullah, Fasee Pun, Chi-Man Mohmand, Muhammad Ismail Mahendran, Rakesh Kumar Khan, Arfat Ahmad Alhammad, Sarah M. Rodrigues, Joel J. P. C. Farouk, Ahmed Universiti Teknologi PETRONAS Computer and Information Sciences department Perak Darul Ridzuan Seri Iskandar32610 Malaysia Avenida da Universidade University of Macau China Sarhad University of Science and IT Department of Computer Science and IT Peshawar Pakistan Rajalakshmi Engineering College Department of Computer Science and Engineering Chennai India Khon Kaen University College of Computing Department of Computer Science Khon Kaen40002 Thailand Princess Nourah Bint Abdulrahman University College of Computer and Information Sciences Department of Computer Sciences Riyadh11671 Saudi Arabia Amazonas State University Manaus Brazil Hurghada University Faculty of Computers and Artificial Intelligence Department of Computer Science Hurghada Egypt

Cloud-based Intelligence of Things is significant for Augmented Enterprise Management Systems. Data integrity auditing is challenging in the intelligence of things environment, mainly when the newer versions in the public cloud environment update existing encrypted data. The related literature on cloud-based intelligence relies on encrypted data uploading or locally handling encryption and decryption using user keys. Considering the security risk, storage constraints at the edge, and realtime environment, both approaches have limited applicability in the intelligence of things environment. This paper presents the Privacy-Aware Secure Data Auditing (PASDA) framework at the cluster head for online data integrity verification. Specifically, the users hide data files by the blinding process with a generation of their corresponding signatures, which achieves data auditing by utilizing homomorphic techniques. A novel automated self-triggering/ Self-auditing-based data integrity auditing system is proposed, which detects the changes made in the cloud-stored data and sends alert messages to the trusted primary cloud server and users. A data dynamics method is developed containing a timestamp with a pointer to store multiple versions of the same file without signatures re-generation for the whole same file. The user is revoked due to prolonged absence or detection of the missed behaviour with system or service expiry. With these data dynamics, the proposed PASDA framework allows CH to regenerate signatures of the revoked user using its membership key for cloud-based stored data access and data integrity auditing. In-depth security analysis and extensive simulations based on comparative performance evaluation attest to the benefits of the proposed PA. © 2014 IEEE.

关键词： Data integrity

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Generating Temporal Contact Graphs Using Random Walkers

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IEEE Transactions on Network science and Engineering 2025年第3期12卷 1649-1659页

作者： Almasan, Anton-David Shvydun, Sergey Scholtes, Ingo Van Mieghem, Piet Delft University of Technology Faculty of Electrical Engineering Mathematics and Computer Science Delft2628 CD Netherlands 50 Würzburg97074 Germany

We study human mobility networks through timeseries of contacts between individuals. Our proposed Random Walkers Induced temporal Graph (RWIG) model generates temporal graph sequences based on independent random walkers that traverse an underlying graph in discrete time steps. Co-location of walkers at a given node and time defines an individual-level contact. RWIG is shown to be a realistic model for temporal human contact graphs, which may place RWIG on a same footing as the Erdos–Renyi (ER) and Barabasi–Albert (BA) models for fixed graphs. Moreover, RWIG is analytically feasible: we derive closed form solutions for the probability distribution of contact graphs. © 2013 IEEE.

关键词： Generative adversarial networks

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