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On Zarankiewicz's Problem for Intersection Hypergraphs of Geometric Objects

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arXiv 2024年

作者： Chan, Timothy M. Keller, Chaya Smorodinsky, Shakhar Siebel School of Computing and Data Science University of Illinois at Urbana-Champaign UrbanaIL61801 United States Department of Computer Science Ariel University Israel Department of Computer Science Ben-Gurion University of the NEGEV Be'er Sheva84105 Israel

The hypergraph Zarankiewicz's problem, introduced by Erdős in 1964, asks for the maximum number of hyperedges in an r-partite hypergraph with n vertices in each part that does not contain a copy of Kt,t, ...,t. Erdős obtained a near optimal bound of O(nr−1/tr−1) for general hypergraphs. In recent years, several works obtained improved bounds under various algebraic assumptions - e.g., if the hypergraph is semialgebraic. In this paper we study the problem in a geometric setting - for r-partite intersection hypergraphs of families of geometric objects. Our main results are essentially sharp bounds for families of axis-parallel boxes in Rd and families of pseudo-discs. For axis-parallel boxes, we obtain the sharp bound Od,t(nr−1( log n/log log n)d−1). The best previous bound was larger by a factor of about (log n)d(2r−1−2). For pseudo-discs, we obtain the bound Ot(nr−1(log n)r−2), which is sharp up to logarithmic factors. As this hypergraph has no algebraic structure, no improvement of Erdős' 60-year-old O(nr−1/tr−1) bound was known for this setting. Futhermore, even in the special case of discs for which the semialgebraic structure can be used, our result improves the best known result by a factor of Ω˜(n2r-2/3r-2). To obtain our results, we use the recently improved results for the graph Zarankiewicz's problem in the corresponding settings, along with a variety of combinatorial and geometric techniques, including shallow cuttings, biclique covers, transversals, and *** Codes 05C35, 05C65 Copyright © 2024, The Authors. All rights reserved.

关键词： Algebra

A General Framework for Clustering and Distribution Matching with Bandit Feedback

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arXiv 2024年

作者： Yavas, Recep Can Huang, Yuqi Tan, Vincent Y.F. Scarlett, Jonathan Descartes Program CNRS@CREATE 138602 Singapore Department of Information Systems and Analytics School of Computing National University of Singapore 119077 Singapore Department of Mathematics Department of Electrical and Computer Engineering National University of Singapore 119077 Singapore Department of Computer Science Department of Mathematics Institute of Data Science National University of Singapore 119077 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 *** Codes 68T05 Copyright © 2024, The Authors. All rights reserved.

关键词： Clustering algorithms

Image Compressive Sensing With Scale-Variable Adaptive Sampling and Hybrid-Attention Transformer Reconstruction

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IEEE Transactions on Multimedia 2025年

作者： Hui, Chen Zhao, Debin Lin, Weisi Liu, Shaohui Jiang, Feng Nanjing University of Information Science and Technology School of Future Technology Nanjing210044 China Harbin Institute of Technology Department of Computer Science and Technology Harbin150001 China Nanyang Technological University College of Computing and Data Science Singapore639798 Singapore

Recently, a large number of image compressive sensing (CS) methods with deep unfolding networks (DUNs) have been proposed. However, existing methods either use fixed-scale blocks for sampling that leads to limited insights into the image content or employ a plain convolutional neural network (CNN) in each iteration that weakens the perception of broader contextual prior. In this paper, we propose a novel DUN (dubbed SVASNet) for image compressive sensing, which achieves scale-variable adaptive sampling and hybrid-attention Transformer reconstruction with a single model. Specifically, for scale-variable sampling, a sampling matrix-based calculator is first employed to evaluate the reconstruction distortion, which only requires measurements without access to the ground truth image. Then, a Block Scale Aggregation (BSA) strategy is presented to compute the reconstruction distortion under block divisions at different scales and select the optimal division scale for sampling. To realize hybrid-attention reconstruction, a dual Cross Attention (CA) submodule in the gradient descent step and a Spatial Attention (SA) submodule in the proximal mapping step are developed. The CA submodule introduces inter-phase inertial forces in the gradient descent, which improves the memory effect between adjacent iterations. The SA submodule integrates local and global prior representations of CNN and Transformer, and explores local and global affinities between dense feature representations. Extensive experimental results show that the proposed SVASNet achieves significant improvements over the state-of-the-art methods. © 1999-2012 IEEE.

关键词： Convolutional neural networks

Public Awareness of Phishing in Indonesia: System Literature Review

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Public Awareness of Phishing in Indonesia: System Literature...

data science and Its Applications (ICoDSA), International Conference on

作者： Anthony William Chandra Zefanya Chandiarta Lutfi Setiyawan Alexander Agung Santoso Gunawan Ivan Sebastian Edbert Cyber Security Program School of Computer Science Bina Nusantara University Jakarta Indonesia Computer Science Department School of Computer Science Bina Nusantara University Jakarta Indonesia

In an era where technology is advancing at an unprecedented rate, the internet has become an indispensable tool for everyday life, offering both convenience and efficiency. While the internet has simplified tasks ranging from communication to information retrieval, it also poses significant challenges in the realm of cyber security. Users are not just potential victims but can also become unwitting perpetrators of cybercrimes. This highlights the urgent need for cyber security awareness among the general populace. Focusing on Indonesia, a nation where cyber security education is still in its infancy, this study aims to assess the level of public awareness regarding the specific threat of phishing. Employing the Systematic Literature Review (SLR) methodology, this research analyzes a comprehensive range of literature encompassing subjects such as phishing, data leakage, broader cyber security threats, and the current state of public awareness about phishing. The objective is to provide a detailed overview of existing knowledge and identify areas where educational efforts should be concentrated. The results indicate a considerable gap in the understanding of phishing among Indonesians. Many studies reviewed did not offer a clear definition or explanation of phishing, creating an environment where individuals are more susceptible to these cyber-attacks. This research underscores the urgent need for targeted educational initiatives aimed at increasing public awareness and understanding of phishing and other cyber threats.

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Contract-Inspired Contest Theory for Controllable Image Generation in Mobile Edge Metaverse

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arXiv 2025年

作者： Liu, Guangyuan Du, Hongyang Wang, Jiacheng Niyato, Dusit Kim, Dong In College of Computing and Data Science The Energy Research Institute @ NTU Interdisciplinary Graduate Program Nanyang Technological University Singapore Department of Electrical and Electronic Engineering University of Hong Kong Hong Kong College of Computing and Data Science Nanyang Technological University Singapore Department of Electrical and Computer Engineering Sungkyunkwan University Korea Republic of

The rapid advancement of immersive technologies has propelled the development of the Metaverse, where the convergence of virtual and physical realities necessitates the generation of high-quality, photorealistic images to enhance user experience. However, generating these images, especially through Generative Diffusion Models (GDMs), in mobile edge computing environments presents significant challenges due to the limited computing resources of edge devices and the dynamic nature of wireless networks. This paper proposes a novel framework that integrates contract-inspired contest theory, Deep Reinforcement Learning (DRL), and GDMs to optimize image generation in these resource-constrained environments. The framework addresses the critical challenges of resource allocation and semantic data transmission quality by incentivizing edge devices to efficiently transmit high-quality semantic data, which is essential for creating realistic and immersive images. The use of contest and contract theory ensures that edge devices are motivated to allocate resources effectively, while DRL dynamically adjusts to network conditions, optimizing the overall image generation process. Experimental results demonstrate that the proposed approach not only improves the quality of generated images but also achieves superior convergence speed and stability compared to traditional methods. This makes the framework particularly effective for optimizing complex resource allocation tasks in mobile edge Metaverse applications, offering enhanced performance and efficiency in creating immersive virtual environments. © 2025, CC BY.

关键词： Mobile edge computing

A Distributed Framework for Large-scale Protein-protein Interaction data Analysis and Prediction Using MapReduce

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IEEE/CAA Journal of Automatica Sinica 2022年第1期9卷 160-172页

作者： Lun Hu Shicheng Yang Xin Luo Huaqiang Yuan Khaled Sedraoui MengChu Zhou School of Computer Science and Technology Dongguan University of TechnologyDongguan 523808China School of Computer Science and Technology Wuhan University of TechnologyWuhan 430070China Chongqing Engineering Research Center of Big Data Application for Smart Cities and Chongqing Key Laboratory of Big Data and Intelligent ComputingChongqing Institute of Green and Intelligent TechnologyChinese Academy of SciencesChongqing 400714China Xinjiang Technical Institute of Physics and Chemistry Chinese Academy of SciencesUrumqi 830000China Center of Research Excellence in Renewable Energy and Power Systems and the Department of Electrical and Computer EngineeringFaculty of EngineeringKing Abdulaziz UniversityJeddah 21589Saudi Arabia Department of Electrical and Computer Engineering New Jersey Institute of TechnologyNewarkNJ 07102 USA

Protein-protein interactions are of great significance for human to understand the functional mechanisms of *** the rapid development of high-throughput genomic technologies,massive protein-protein interaction(PPI)data have been generated,making it very difficult to analyze them *** address this problem,this paper presents a distributed framework by reimplementing one of state-of-the-art algorithms,i.e.,CoFex,using *** do so,an in-depth analysis of its limitations is conducted from the perspectives of efficiency and memory consumption when applying it for large-scale PPI data analysis and *** solutions are then devised to overcome these *** particular,we adopt a novel tree-based data structure to reduce the heavy memory consumption caused by the huge sequence information of *** that,its procedure is modified by following the MapReduce framework to take the prediction task distributively.A series of extensive experiments have been conducted to evaluate the performance of our framework in terms of both efficiency and *** results well demonstrate that the proposed framework can considerably improve its computational efficiency by more than two orders of magnitude while retaining the same high accuracy.

关键词： Distributed computing large-scale prediction machine learning MapReduce protein-protein interaction(PPI)

A comparative study of augmented inverse propensity weighted estimators using outcome-oriented covariate selection via penalization with outcome-adaptive lasso

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arXiv 2024年

作者： Hongo, Wataru Ando, Shuji Tsuchida, Jun Sozu, Takashi Department of Information and Computer Technology Graduate School of Engineering Tokyo University of Science Tokyo Japan Advanced Quantitative Sciences Development Novartis Pharma K.K. Tokyo Japan Department of Information Sciences Faculty of Science and Technology Tokyo University of Science Chiba Japan Department of Data Science Faculty of Data Science Kyoto Women’s University Kyoto Japan Department of Information and Computer Technology Faculty of Engineering Tokyo University of Science Tokyo Japan

Background: When estimating causal effects from observational data with numerous covariates, employing penalized covariate selection can improve the estimation efficiency. Outcome-oriented covariate selection, which involves selecting covariates related to the outcome, can enhance efficiency, even for propensity score (PS) methods. For outcome-oriented covariate selection in PS models, outcome-adaptive lasso can be used for penalization with the oracle property. The performance of inverse propensity weighted (IPW) estimators using outcome-adaptive lasso was shown to be superior to that of the IPW estimators using other covariate selection methods for parametric models. However, the augmented IPW (AIPW) estimator is typically employed as a doubly robust estimator for the average treatment effect, which requires both PS and outcome models. Despite this, which covariate selection method for outcome models should be combined with the outcome-adaptive lasso to form the AIPW estimator remains unclear. We evaluated the performance of the AIPW estimators using the outcome-adaptive lasso for PS models and various outcome-oriented covariate selection via penalization for outcome models. Methods: We conducted numerical experiments to evaluate the performance of AIPW estimators using various covariate selection via penalization. In numerical experiments, we assessed bias, standard error, and root-mean-squared error. We applied the AIPW estimators to a clinical trial dataset for reference, comparing their point estimates and standard errors obtained via bootstrap. Results: In the numerical experiments, the performance of the AIPW estimators using outcome-oriented covariate selection via penalization with the oracle property for both PS and outcome models was superior to that of the other estimators and similar to that of the AIPW estimator, which relies on true confounders and outcome predictors. In contrast, the bias of the AIPW estimators not relying on the oracle property was

关键词： Mean square error

Explainable Convolutional Neural Network for Phenotype Prediction from Genotype

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Explainable Convolutional Neural Network for Phenotype Predi...

International Conference on e-health Networking, Applications and Services (HealthCom)

作者： Maxwell Sam Richard Annan Pei Yang Mohd Anwar Kristen Rhinhardt Letu Qingge Department of Computer Science North Carolina A&T State University Greensboro NC USA Department of Computer Science Qinghai University Xining China Department of Computational Data Science and Engineering North Carolina A&T State University Greensboro NC USA

ISBN: (数字)9798350350548

ISBN: (纸本)9798350350555

In the realm of precision medicine, the prediction of phenotypic traits from genotype data plays a pivotal role in understanding disease susceptibility, drug response, and overall health outcomes. Machine learning models trained on genotype data offer promising avenues for phenotype prediction. However, the black-box nature of these models often hinders interpretabil-ity, raising concerns about their reliability and trustworthiness in clinical decision-making. This paper explores the application of SHapley Additive exPlanations (SHAP) and Local Interpretable Model-agnostic Explanations (LIME) on our existing CNN model to identify important features from genotype that contribute to phenotype prediction in yeast data. Both techniques on machine learning models include improving model transparency and interpretability by providing insights into prediction mechanisms. LIME focuses on local interpretability, explaining individual predictions tailored to specific instances swiftly, while SHAP computes feature importance using Shapley values, offering a comprehensive understanding of feature contributions. Our dataset contains 4,390 samples, each with 28,220 features in genotype which are used in the training, testing and validation of our existing CNN model. In our analysis of 20 phenotypes, we identify the number of relevant important features using SHAP, LIME methods, and the intersection which not only improves the Mean Squared Error (MSE) values compared to using all the features in the genotype data, but also the model training time.

关键词： Training Analytical models Phenotypes Additives Computational modeling Machine learning Predictive models data models Convolutional neural networks Genotypes

COVID-19 Twitter Sentiment Classification Using Hybrid Deep Learning Model Based on Grid Search Methodology

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arXiv 2024年

作者： Tembhurne, Jitendra V. Agrawal, Anant Lakhotia, Kirtan Department of Computer Science & Engineering Indian Institute of Information Technology Nagpur India Department of Computer Science & Data Science Shri Ramdeobaba College of Engineering & Management Nagpur India Department of Information Technology Institute of Engineering and Technology Devi Ahilya Vishwavidyalaya Indore India

In the contemporary era, social media platforms amass an extensive volume of social data contributed by their users. In order to promptly grasp the opinions and emotional inclinations of individuals’ regarding a product or event, it becomes imperative to perform sentiment analysis on the user-generated content. Microblog comments often encompass both lengthy and concise text entries, presenting a complex scenario. This complexity is particularly pronounced in extensive textual content due to its rich content and intricate word interrelations compared to shorter text entries. Sentiment analysis of public opinion shared on social networking websites such as Facebook or Twitter has evolved and found diverse applications. However, several challenges remain to be tackled in this field. The hybrid methodologies have emerged as promising models for mitigating sentiment analysis errors, particularly when dealing with progressively intricate training data. In this article, to investigate the hesitancy of COVID-19 vaccination, we propose eight different hybrid deep learning models for sentiment classification with an aim of improving overall accuracy of the model. The sentiment prediction is achieved using embedding, deep learning model and grid search algorithm on Twitter COVID-19 dataset. According to the study, public sentiment towards COVID-19 immunisation appears to be improving with time, as evidenced by the gradual decline in vaccine reluctance. Through extensive evaluation, proposed model reported an increased accuracy of 98.86%, outperforming other models. Specifically, the combination of BERT + CNN + GS yield the highest accuracy, while the combination of GloVe + Bi-LSTM + CNN + GS follows closely behind with an accuracy of 98.17%. In addition, increase in accuracy in the range of 2.11% to 14.46% is reported by the proposed model in comparisons with existing works. Thus, the combination of embedding and grid search increases the accuracy by using deep learning model

关键词： Sentiment analysis