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arXiv 2025年

作者： Gifford, Robert Eisenklam, Abby Bondar, Georgiy A. Cai, Yifan Sial, Tushar Phan, Linh Thi Xuan Halder, Abhishek Department of Computer and Information Science University of Pennsylvania United States Department of Applied Mathematics University of California Santa Cruz United States Department of Aerospace Engineering Iowa State University United States

As multicore hardware is becoming increasingly common in real-time systems, traditional scheduling techniques that assume a single worst-case execution time for a task are no longer adequate, since they ignore the impact of shared resources on execution time. When tasks execute concurrently on different cores, their execution times often vary substantially with their allocated budgets of shared resources, such as cache and memory bandwidth. Even under a specific resource allocation, the resource use pattern of a task also changes with time during a job execution. It is therefore important to consider the relationship between multicore resources and execution time in task modeling and scheduling algorithm design. In this paper, we propose a much more precise execution model for DAG-based real-time tasks that captures the time-varying resource use characteristics of a task under different budgets of shared resources. We present a generative resource profiling algorithm that efficiently predicts, from limited measurement data, the resource profile of a task at any time during its execution under a given resource budget. The generative profiles can then be used to construct the execution models for tasks, using which one can make informed resource allocation decisions. We further introduce a multicore resource allocation and deadline decomposition co-design technique for DAG-based tasks that leverages the generated execution models to jointly allocate resources and deadlines to subtasks, to maximize resource efficiency and schedulability. Our evaluation results show that our generative profiling algorithm achieves high accuracy while being efficient, and that our co-allocation technique substantially improves schedulability compared to a state-of-the-art deadline decomposition method. Copyright © 2025, The Authors. All rights reserved.

关键词： Budget control

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THE REVISED BOOMERANG CONNECTIVITY TABLES AND THEIR CONNECTION TO THE DIFFERENCE DISTRIBUTION TABLE

arXiv

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arXiv 2024年

作者： Garg, Kirpa Hasan, Sartaj Ul Riera, Constanza StăNica, Pantelimon Department of Mathematics Indian Institute of Technology Jammu Jammu 181221 India Department of Computer Science Electrical Engineering and Mathematical Sciences Western Norway University of Applied Sciences Bergen5020 Norway Applied Mathematics Department Naval Postgraduate School MontereyCA93943 United States

It is well-known that functions over finite fields play a crucial role in designing substitution boxes (S-boxes) in modern block ciphers. In order to analyze the security of an S-box, recently, three new tables have been introduced: the Extended Boomerang Connectivity Table (EBCT), the Lower Boomerang Connectivity Table (LBCT), and the Upper Boomerang Connectivity Table (UBCT). In fact, these tables offer improved methods over the usual Boomerang Connectivity Table (BCT) for analyzing the security of S-boxes against boomerang-style attacks. Here, we put in context these new EBCT, LBCT, and UBCT concepts by connecting them to the DDT for a differentially δ-uniform function and also determine the EBCT, LBCT, and UBCT entries of three classes of differentially 4-uniform power permutations, namely, Gold, Kasami and Bracken-Leander. We also determine the Double Boomerang Connectivity Table (DBCT) entries of the Gold function. As byproducts of our approach, we obtain some previously published results quite *** Codes 12E20, 11T06, 94A60 Copyright © 2024, The Authors. All rights reserved.

关键词： Delta functions

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Budgeted Dominating Sets in Uncertain Graphs 46

Budgeted Dominating Sets in Uncertain Graphs

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46th International Symposium on Mathematical Foundations of computer science, MFCS 2021

作者： Choudhary, Keerti Cohen, Avi Narayanaswamy, N.S. Peleg, David Vijayaragunathan, R. Indian Institute of Technology Delhi India Tel Aviv University Israel Department of Computer Science and Engineering Iit Madras India Department of Computer Science and Applied Mathematics Weizmann Institute of Science Rehovot Israel

ISBN: (纸本)9783959772013

We study the Budgeted Dominating Set (BDS) problem on uncertain graphs, namely, graphs with a probability distribution p associated with the edges, such that an edge e exists in the graph with probability p(e). The input to the problem consists of a vertex-weighted uncertain graph G = (V,E, p, ω) and an integer budget (or solution size) k, and the objective is to compute a vertex set S of size k that maximizes the expected total domination (or total weight) of vertices in the closed neighborhood of S. We refer to the problem as the Probabilistic Budgeted Dominating Set (PBDS) problem. In this article, we present the following results on the complexity of the PBDS problem. 1. We show that the PBDS problem is NP-complete even when restricted to uncertain trees of diameter at most four. This is in sharp contrast with the well-known fact that the BDS problem is solvable in polynomial time in trees. We further show that PBDS is W[1]-hard for the budget parameter k, and under the Exponential time hypothesis it cannot be solved in no(k) time. 2. We show that if one is willing to settle for (1 ) approximation, then there exists a PTAS for PBDS on trees. Moreover, for the scenario of uniform edge-probabilities, the problem can be solved optimally in polynomial time. 3. We consider the parameterized complexity of the PBDS problem, and show that Uni-PBDS (where all edge probabilities are identical) is W[1]-hard for the parameter pathwidth. On the other hand, we show that it is FPT in the combined parameters of the budget k and the treewidth. 4. Finally, we extend some of our parameterized results to planar and apex-minor-free graphs. Our first hardness proof (Thm. 1) makes use of the new problem of k-Subset ΣΠ Maximization (k-SPM), which we believe is of independent interest. We prove its NP-hardness by a reduction from the well-known k-SUM problem, presenting a close relationship between the two problems. © Keerti Choudhary, Avi Cohen, N. S. Narayanaswamy, David Peleg, and R.

关键词： Probability distributions

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Blockchain-based Privacy-Preserving Public Key Searchable Encryption with Strong Traceability

arXiv

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arXiv 2023年

作者： Han, Yue Han, Jinguang Meng, Weizhi Lai, Jianchang Wu, Ge School of Cyber Science and Engineering Southeast University Jiangsu Nanjing210096 China Department of Applied Mathematics and Computer Science Technical University of Denmark Kongens Lyngby2800 Denmark

Public key searchable encryption (PKSE) scheme allows data users to search over encrypted data. To identify illegal users, many traceable PKSE schemes have been proposed. However, existing schemes cannot trace the keywords which illegal users searched and protect users' privacy simultaneously. In some practical applications, tracing both illegal users' identities and the keywords which they searched is quite important to against the abuse of data. It is a challenge to bind users' identities and keywords while protecting their privacy. Moreover, existing traceable PKSE schemes do not consider the unforgeability and immutability of trapdoor query records, which can lead to the occurrence of frame-up and denying. In this paper, to solve these problems, we propose a blockchain-based privacy-preserving PKSE with strong traceability (BP3KSEST) scheme. Our scheme provides the following features: (1) authorized users can authenticate to trapdoor generation center and obtain trapdoors without releasing their identities and keywords;(2) when data users misbehave in the system, the trusted third party (TTP) can trace both their identities and the keywords which they searched;(3) trapdoor query records are unforgeable;(4) trapdoor query records are immutable because records are stored in blockchain. Notably, this scheme is suitable to the scenarios where privacy must be considered, e.g., electronic health record (EHR). We formalize both the definition and security model of our BP3KSEST scheme, and present a concrete construction. Furthermore, the security of the proposed scheme is formally proven. Finally, the implementation and evaluation are conducted to analyze its efficiency. Copyright © 2023, The Authors. All rights reserved.

关键词： Blockchain

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Cross-Domain Hyperspectral Image Classification Based on Graph Convolutional Networks

Cross-Domain Hyperspectral Image Classification Based on Gra...

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IEEE International Symposium on Geoscience and Remote Sensing (IGARSS)

作者： Yushan Li Minchao Ye Yuntao Qian Qipeng Qian College of Information Engineering China Jiliang University China College of Computer Science Zhejiang University China Department of Applied Mathematics University of Arizona USA

A major challenge in hyperspectral image (HSI) classification is the small-sample-size problem. Cross-domain information can help solve the problem. In cross-domain HSI classification, the source domain has many samples, while the target domain has fewer samples. Transfer learning can transfer knowledge from the source domain to the target domain. The source and target domains are mostly captured by different sensors and thus come from different feature spaces. Heterogeneous transfer learning can solve this problem. This paper proposes a transfer learning method based on a crossdomain graph convolutional network (CD-GCN). A class co-occurrence semantic graph is built between heterogeneous spaces of source and target domains. Then graph convolutional network (GCN) is adopted to learn the features of graphs. To handle the different feature dimensions, a feature alignment subnet is proposed. By combining a feature alignment subnet and a GCN feature extraction subnet, the proposed model CD-GCN transfers knowledge between heterogeneous domains. Experiments on two cross-domain HSI datasets prove that CD-GCN overperforms many transfer learning methods.

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Cross-Domain Hyperspectral Image Classification Based on Transformer

Cross-Domain Hyperspectral Image Classification Based on Tra...

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IEEE International Symposium on Geoscience and Remote Sensing (IGARSS)

作者： Jiawei Ling Minchao Ye Yuntao Qian Qipeng Qian College of Information Engineering China Jiliang University China College of Computer Science Zhejiang University China Department of Applied Mathematics University of Arizona USA

Small-sample-size problem is a big challenge in hyperspectral image (HSI) classification. Deep learning-based methods, especially Transformer, may need more training samples to train a satisfactory model. Cross-domain classification has been proven to be effective in handling the small-sample-size problem. In two HSI scenes sharing the same land-cover classes, one with sufficient labeled samples is called the source domain, while the other with limited labeled samples is called the target domain. Thus, the information on the source domain could help the target domain improve classification performance. This paper proposes a cross-domain Vision Transformer (CD-ViT) method for heterogeneous HSI classification. CD-ViT maps the source samples to the target domain for supplementing training samples. In addition, cross-attention is used to align the source and target features. Moreover, knowledge distillation is employed to learn more transferable information. Experiments on three different cross-domain HSI datasets demonstrate the effectiveness of the proposed approach.

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A novel lexicographical-based method for trapezoidal neutrosophic linear programming problem

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Neutrosophic Sets and Systems 2021年 46卷 151-179页

作者： Das, Sapan Kumar Edalatpanah, S.A. Dash, Jatindra Kumar Department of Revenue Ministry of Finance Govt. of India India Department ofT Applied Mathematics Ayandegan Institute of Higher Education Tonekabon Iran Department of Computer Science and Engineering SRM University-AP Amravati India

The aim of this paper is to introduce a simplified presentation of a new computing procedure for solving trapezoidal neutrosophic linear programming (TrNLP) problem under uncertainties. Therefore, we firstly define the concept of single valued neutrosophic (SVN) numbers and ranking functions. A new strategy is planned for solving the NLP problem without any ranking function. The planned strategy is depends on multi-objective LP (MOLP) issue and lexicographic order (LO). By following the means of planned strategy, the problem is changed into crisp LP (CLP) problem. In addition to this, a theoretical analysis is provided. Numerical examples are illustrated the proposed method and the consequences are in contrast with the distinct choice methods. The outcome shows that the proposed technique defeats the deficiencies and constraints of the existing method. © 2021

关键词： Linear programming

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AI-Powered Healthcare with Advanced Graph Convolutional Neural Networks and SVM for Diagnosis

AI-Powered Healthcare with Advanced Graph Convolutional Neur...

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Integrated Circuits and Communication Systems (ICICACS), IEEE International Conference on

作者： A. Mahendran Sridhar Babu Nalla Lalit Mohan Trivedi S Mohankumar D.R Prince Williams Manne Renuka Department of Computer Science Sona College of Arts and Science Salem India Department of Computer Science and Engineering SR University Hyderabad India Department of Ash Mathematics Moradabad Institute of Technology Moradabad India Department of Computer Science and Engineering Erode Sengunthar Engineering College Perundurai India Department of Information Technology College of Computing and Information Sciences University of Technology and Applied Science- Sohar Sohar Sultanate of Oman Department of Electronics and Communication Engineering Vidya Jyothi Institute of Technology Hyderabad India

ISBN: (数字)9798331508456

ISBN: (纸本)9798331508463

Medical treatment costs are rising for many reasons. These include an increasing world population, an aging population, greater healthcare needs, more diseases, and inflation. With AI's emergence in navigation, voice recognition, image lesion segmentation, ride-sharing, and smartphone personal assistants, the healthcare industry has quickly adopted it. Initial preprocessing includes standardization, missing values, and discarding anomalies. Next, the model is trained using the obtained features. Spearman correlation, a rank-based nonparametric statistic, can quantify monotonic correlation strength and direction. Even with missing values, the Graph Convolutional Network (GCN) performs well in model training. The suggested model outperformed state-of-the-art alternatives with 93.48% accuracy. It outperformed TransLSTM and GraphSAGE on complex datasets. This research stresses the system's ability to improve healthcare forecast accuracy through preprocessing, feature extraction, and GCN model training. Results show that AI can improve healthcare analytics despite lacking data.

关键词： Training Support vector machines Correlation Accuracy Graph convolutional networks Medical services Feature extraction Integrated circuit modeling Artificial intelligence Stress

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Towards Fair and Robust Face Parsing for Generative AI: A Multi-Objective Approach

arXiv

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arXiv 2025年

作者： Abraham, Sophia J. Hauenstein, Jonathan D. Scheirer, Walter J. Department of Computer Science and Engineering University of Notre Dame Notre Dame IN46556 United States Department of Applied and Computational Mathematics and Statistics University of Notre Dame Notre Dame IN46556 United States

Face parsing is a fundamental task in computer vision, enabling applications such as identity verification, facial editing, and controllable image synthesis. However, existing face parsing models often lack fairness and robustness, leading to biased segmentation across demographic groups and errors under occlusions, noise, and domain shifts. These limitations affect downstream face synthesis, where segmentation biases can degrade generative model outputs. We propose a multi-objective learning framework that optimizes accuracy, fairness, and robustness in face parsing. Our approach introduces a homotopy-based loss function that dynamically adjusts the importance of these objectives during training. To evaluate its impact, we compare multi-objective and single-objective U-Net models in a GAN-based face synthesis pipeline (Pix2PixHD). Our results show that fairness-aware and robust segmentation improves photorealism and consistency in face generation. Additionally, we conduct preliminary experiments using ControlNet, a structured conditioning model for diffusion-based synthesis, to explore how segmentation quality influences guided image generation. Our findings demonstrate that multi-objective face parsing improves demographic consistency and robustness, leading to higher-quality GAN-based synthesis.1 © 2025, CC BY-NC-ND.

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Mathematical Modeling of Dependences Influence of Lamé Coefficients by Concentration and Temperature on Distributions of Spatial Soil Massif Displacements 11

Mathematical Modeling of Dependences Influence of Lamé Coef...

引用

11th International Conference on Advanced computer Information Technologies, ACIT 2021

作者： Zhukovska, Nataliia Zhukovskyy, Viktor Safonyk, Andrii National University of Water and Environmental Engineering Department of Computer Science and Applied Mathematics Rivne Ukraine National University of Water and Environmental Engineering Department of Automation Electrical Engineering and Computer-Integrated Technologies Rivne Ukraine

The influence of the dependences of the Lamé coefficients on concentration and temperature on the distributions of the displacements along the axes Ox, Oy and Oz was investigated in the article. Numerous mathemat... 详细信息

关键词： Finite difference method

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