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EXISTENCE OF WEAK SOLUTIONS FOR OBSTACLE PROBLEMS WITH VARIABLE GROWTH IN ORLICZ-SOBOLEV SPACES

Journal of Mathematical sciences (United States) 2024年第2期289卷 155-167页

作者： Allalou, Mouad Ait Temghart, Said Raji, Abderahmane Applied Mathematics and Scientific Computing Laboratory Faculty of Science and Technics Sultan Moulay Slimane University BP 523 Beni Mellal 23000 Morocco National Higher School for Artificial Intelligence and Data Sciences Ibn Zohr University Taroudant 83000 Morocco

This article delves into the exploration of weak solutions’ existence within the context of obstacle problems associated with the following inequality: ∫ΩV(z,w,Dw):D(v-w)dz≥g,v-w, where v lies in a convex set FO,B... 详细信息

This article delves into the exploration of weak solutions’ existence within the context of obstacle problems associated with the following inequality: ∫_ΩV(z,w,Dw):D(v-w)dz≥g,v-w, where v lies in a convex set F_O,B. The primary methodology employed in this investigation involves the utilization of You ng’s measure theory, complemented by a theorem originating from Kinderlehrer and Stampacchia, specifically tailored for reflexive Orlicz-Sobolev spaces. © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2024.

关键词： 28A33 35J88 46E30 46E35 Kinderlehrer and Stampacchia Obstacle problem Orlicz-Sobolev spaces Young’s measures

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Local and Global Feature-Aware Dual-Branch Networks for Plant Disease Recognition

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植物表型组学（英文） 2024年第4期6卷 828-840页

作者： Jianwu Lin Xin Zhang Yongbin Qin Shengxian Yang Xingtian Wen Tomislav Cernava Quirico Migheli Xiaoyulong Chen Text Computing & Cognitive Intelligence Engineering Research Center of National Education Ministry College of Computer Science and TechnologyGuizhou UniversityGuiyang 550025China State Key Laboratory of Public Big Data College of Computer Science and TechnologyGuizhou UniversityGuiyang 550025China College of Big Data and Information Engineering Guizhou UniversityGuiyang 550025China Guizhou-Europe Environmental Biotechnology and Agricultural Informatics Oversea Innovation Center in Guizhou University Guizhou Provincial Science and Technology DepartmentGuiyang 550025China Text Computing & Cognitive Intelligence Engineering Research Center of National Education Ministry College of Computer Science and TechnologyGuizhou UniversityGuiyang 550025China State Key Laboratory of Public Big Data College of Computer Science and TechnologyGuizhou UniversityGuiyang 550025China College of Big Data and Information Engineering Guizhou UniversityGuiyang 550025China Guizhou-Europe Environmental Biotechnology and Agricultural Informatics Oversea Innovation Center in Guizhou University Guizhou Provincial Science and Technology DepartmentGuiyang 550025China College of Big Data and Information Engineering Guizhou UniversityGuiyang 550025China School of Biological Sciences Faculty of Environmental and Life SciencesUniversity of SouthamptonSouthampton S017 1BJUK Dipartimento di Agraria and NRD-Nucleo di Ricerca sulla Desertificazione Università degli Studi di SassariSassariItaly Guizhou-Europe Environmental Biotechnology and Agricultural Informatics Oversea Innovation Center in Guizhou University Guizhou Provincial Science and Technology DepartmentGuiyang 550025China College of Life Sciences Guizhou UniversityGuiyang 550025China

Accurate identification of plant diseases is important for ensuring the safety of agricultural *** neural networks(CNNs)and visual transformers(VTs)can extract effective representations of images and have been widely used for the intelligent recognition of plant disease ***,CNNs have excellent local perception with poor global perception,and VTs have excellent global perception with poor local *** makes it difficult to further improve the performance of both CNNs and VTs on plant disease recognition *** this paper,we propose a local and global feature-aware dual-branch network,named LGNet,for the identification of plant *** specifically,we first design a dual-branch structure based on CNNs and VTs to extract the local and global ***,an adaptive feature fusion(AFF)module is designed to fuse the local and global features,thus driving the model to dynamically perceive the weights of different ***,we design a hierarchical mixed-scale unit-guided feature fusion(HMUFF)module to mine the key information in the features at different levels and fuse the differentiated information among them,thereby enhancing the model's multiscale perception ***,extensive experiments were conducted on the Al Challenger 2018 dataset and the self-collected corn disease(SCD)*** experimental results demonstrate that our proposed LGNet achieves state-of-the-art recognition performance on both the Al Challenger 2018 dataset and the SCD dataset,with accuracies of 88.74％and 99.08％,respectively.

关键词： global plant disease dual-branch feature-aware local networks recognition

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Facilities Utilization Framework for Universal Design in Tourism 13

Facilities Utilization Framework for Universal Design in Tou...

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13th IIAI International Congress on Advanced Applied Informatics Winter, IIAI-AAI-Winter 2022

作者： Tongtep, Nattapong Charoenporn, Thatsanee College of Computing Prince of Songkla University Phuket Thailand Musashino University AAII Faculty of Data Science Tokyo Japan

ISBN: (纸本)9798350309928

A suitable environment for people with disabilities has widely campaigned around the world. There are many attempts to mobilize the community to help people with disabilities can enjoy their lives equally. This paper, therefore, presents our initiative on adapting the universal design concept to develop a facilities utilization framework for tourist spots. The system provides information about places and various necessary details of the facilities and services in accordance with the unique needs of people with disabilities in the form of web applications. The paper presents the concept of development, related theories, and the result of applying the information system in practice. The preliminary usability evaluation results are at an outstanding level. Collecting more data on facilities and usage patterns are ongoing task with a focus on monitoring and suggesting the coverage and access rate of all groups of people in the public area. © 2022 IEEE.

关键词： Tourism

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Outlier Robust Multivariate Polynomial Regression 32

Outlier Robust Multivariate Polynomial Regression

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32nd Annual European Symposium on Algorithms, ESA 2024

作者： Arora, Vipul Bhattacharyya, Arnab Boban, Mathews Guruswami, Venkatesan Kelman, Esty School of Computing National University of Singapore Singapore Department of EECS Department of Mathematics University of California BerkeleyCA United States CSAIL Massachusetts Institute of Technology CambridgeMA United States Department of Computer Science Faculty of Computing & Data Sciences Boston University MA United States

ISBN: (纸本)9783959773386

We study the problem of robust multivariate polynomial regression: let p: Rn → R be an unknown n-variate polynomial of degree at most d in each variable. We are given as input a set of random samples (xi, yi) ∈ [−1, 1]n × R that are noisy versions of (xi, p(xi)). More precisely, each xi is sampled independently from some distribution χ on [−1, 1]n, and for each i independently, yi is arbitrary (i.e., an outlier) with probability at most ρ n log d), where the hidden constant depends on n, if χ is the n-dimensional Chebyshev distribution. The sample complexity is On(d2n log d), if the samples are drawn from the uniform distribution instead. The approximation error is guaranteed to be at most O(σ), and the run-time depends on log(1/σ). In the setting where each xi and yi are known up to N bits of precision, the run-time's dependence on N is linear. We also show that our sample complexities are optimal in terms of dn. Furthermore, we show that it is possible to have the run-time be independent of 1/σ, at the cost of a higher sample complexity. © Vipul Arora, Arnab Bhattacharyya, Mathews Boban, Venkatesan Guruswami, and Esty Kelman;licensed under Creative Commons License CC-BY 4.0.

关键词： Logistic regression

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Mobility-aware Seamless Service Migration and Resource Allocation in Multi-edge IoV Systems

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IEEE Transactions on Mobile computing 2025年第7期24卷 6315-6332页

作者： Chen, Zheyi Huang, Sijin Min, Geyong Ning, Zhaolong Li, Jie Zhang, Yan Fuzhou University College of Computer and Data Science Fuzhou350116 China Ministry of Education Engineering Research Center of Big Data Intelligence Fuzhou350002 China Fuzhou University Fujian Key Laboratory of Network Computing and Intelligent Information Processing Fuzhou350116 China University of Exeter Department of Computer Science Faculty of Environment Science and Economy ExeterEX4 4QF United Kingdom Chongqing University of Posts and Telecommunications School of Communications and Information Engineering Chongqing400065 China Shanghai Jiao Tong University Department of Computer Science and Engineering Shanghai200240 China University of Oslo Department of Informatics Oslo0316 Norway

Mobile Edge computing (MEC) offers low-latency and high-bandwidth support for Internet-of-Vehicles (IoV) applications. However, due to high vehicle mobility and finite communication coverage of base stations, it is hard to maintain uninterrupted and high-quality services without proper service migration among MEC servers. Existing solutions commonly rely on prior knowledge and rarely consider efficient resource allocation during the service migration process, making it hard to reach optimal performance in dynamic IoV environments. To address these important challenges, we propose SR-CL, a novel mobility-aware seamless Service migration and Resource allocation framework via Convex-optimization-enabled deep reinforcement Learning in multi-edge IoV systems. First, we decouple the Mixed Integer Nonlinear Programming (MINLP) problem of service migration and resource allocation into two sub-problems. Next, we design a new actor-critic-based asynchronous-update deep reinforcement learning method to handle service migration, where the delayed-update actor makes migration decisions and the one-step-update critic evaluates the decisions to guide the policy update. Notably, we theoretically derive the optimal resource allocation with convex optimization for each MEC server, thereby further improving system performance. Using the real-world datasets of vehicle trajectories and testbed, extensive experiments are conducted to verify the effectiveness of the proposed SR-CL. Compared to benchmark methods, the SR-CL achieves superior convergence and delay performance under various scenarios. © 2025 IEEE.

关键词： Convex optimization

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Deep Learning for Smart Grid Application: Addressing data Scarcity Challenges and Enhancing Load Forecasting Efficiency

Deep Learning for Smart Grid Application: Addressing Data Sc...

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International Conference on Artificial Intelligence and Its Practical Applications in the Age of Digital Transformation, I2COMSAPP 2024

作者： Amalou, Ibtissam Mouhni, Naoual Abdali, Abdelmounim Jakimi, Abdeslam Tourad, Mohamedou Cheikh CISIEV FSTG University Cadi Ayyad Marrakech Morocco LAMIGEP EMSI Marrakech Marrakech Morocco GL-ISI Team Faculty of Science and Technology Errachidia Moulay Ismail University Meknes Morocco Scientific Computing Computer Science and Data Science Research Unit University of Nouakchott Nouakchott Mauritania

ISBN: (纸本)9783031714252

The surge in domestic electricity consumption necessitates innovative solutions for the integration of smart grids. Traditional automation methods prove insufficient for handling unforeseen challenges and sustainability issues, especially with the increasing adoption of renewable energy sources. This paper reviews the application of deep learning (DL) methods to smart grids. The research explores various DL approaches employed in smart grid data collection, such as generative adversarial networks, convolutional neural networks (CNNs), auto-encoders, restricted Boltzmann machines, and multilayer perceptron models. These techniques aim to refine features, detect electricity theft, and identify patterns related to false data injection attacks and energy consumption. data scarcity emerges as a critical challenge impacting smart grid data management, affecting forecasting accuracy, operational insights, efficiency, and innovation. The study advocates for investments in advanced data collection technologies and taking advantage of machine learning techniques to optimize the use of available data. To demonstrate the impact of data scarcity on DL model efficiency, a Machine learning benchmark models are trained for energy consumption forecasting. Results reveal the significance of training models with sufficient data reaching a minimization of 3.1149 for validation mean squared error of one of the models (CNN_LSTM) by increasing the use of training data to 75% of the Individual household electric power consumption dataset, emphasizing the need to address data scarcity challenges for effective model training. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Adversarial machine learning

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Privacy Enhanced Mobile User Authentication Method Using Motion Sensors

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Computer Modeling in Engineering & sciences 2024年第3期138卷 3013-3032页

作者： Chunlin Xiong Zhengqiu Weng Jia Liu Liang Gu Fayez Alqahtani Amr Gafar Pradip Kumar Sharma Shenzhen Institutes of Advanced Technology Chinese Academy of SciencesShenzhen518052China Sangfor Technologies Inc. Shenzhen518055China School of Data Science and Artificial Intelligence Wenzhou University of TechnologyWenzhou325035China College of Computer Science&Technology Zhejiang University of TechnologyHangzhou310023China Software Engineering Department College of Computer and Information SciencesKing Saud UniversityRiyadh12372Saudi Arabia Math&Computer Science Department Faculty of ScienceMenofia UniversityShebin El-KomEgypt Computing Science Department University of AberdeenAberdeenUK

With the development of hardware devices and the upgrading of smartphones,a large number of users save privacy-related information in mobile devices,mainly smartphones,which puts forward higher demands on the protection of mobile users’privacy *** present,mobile user authenticationmethods based on humancomputer interaction have been extensively studied due to their advantages of high precision and non-perception,but there are still shortcomings such as low data collection efficiency,untrustworthy participating nodes,and lack of *** this end,this paper proposes a privacy-enhanced mobile user authentication method with motion sensors,which mainly includes:(1)Construct a smart contract-based private chain and federated learning to improve the data collection efficiency of mobile user authentication,reduce the probability of the model being bypassed by attackers,and reduce the overhead of data centralized processing and the risk of privacy leakage;(2)Use certificateless encryption to realize the authentication of the device to ensure the credibility of the client nodes participating in the calculation;(3)Combine Variational Mode Decomposition(VMD)and Long Short-TermMemory(LSTM)to analyze and model the motion sensor data of mobile devices to improve the accuracy of model *** experimental results on the real environment dataset of 1513 people show that themethod proposed in this paper can effectively resist poisoning attacks while ensuring the accuracy and efficiency of mobile user authentication.

关键词： Mobile authentication blockchain federated learning smart contract certificateless encryption VMD LSTM

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FL@FM-TheWebConf’24: International Workshop on Federated Foundation Models for the Web 24

FL@FM-TheWebConf’24: International Workshop on Federated Fo...

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33rd Companion of the ACM World Wide Web Conference, WWW 2023

作者： King, Irwin Long, Guodong Xu, Zenglin Yu, Han Department of Computer Science and Engineering The Chinese University of Hong Kong Hong Kong Hong Kong Faculty of Engineering and IT University of Technology Sydney Australia School of Computer Science and Technology Harbin Institute of Technology Shenzhen China College of Computing and Data Science Nanyang Technological University Singapore Singapore

ISBN: (纸本)9798400701726

In the evolving landscape of machine learning research, two significant developments have emerged to prominence: foundation models and federated learning. The FL@FM-TheWebConf’24 workshop provides an exciting forum for researchers and practitioners to discuss promising works and chart potential trajectories forward for this emerging field. © 2024 Copyright held by the owner/author(s). Publication rights licensed to ACM.

关键词： Frequency modulation

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Surveying neuro-symbolic approaches for reliable artificial intelligence of things

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Journal of Reliable Intelligent Environments 2024年第3期10卷 257-279页

作者： Lu, Zhen Afridi, Imran Kang, Hong Jin Ruchkin, Ivan Zheng, Xi Faculty of Data Science City University of Macau Estrada de Coelho do Amaral 999078 China School of Computing Macquarie University Balaclava Rd SydneyNSW2109 Australia Computer Science University of California Los Angeles 404 Westwood Plaza Los AngelesCA90095 United States Electrical and Computer Engineering University of Florida 1889 Museum Rd GainesvilleFL32611 United States

The integration of Artificial Intelligence (AI) with the Internet of Things (IoT), known as the Artificial Intelligence of Things (AIoT), enhances the devices’ processing and analysis capabilities and disrupts such sectors as healthcare, industry, and oil. However, AIoT’s complexity and scale are challenging for traditional machine learning (ML). Deep learning offers a solution but has limited testability, verifiability, and interpretability. In turn, the neuro-symbolic paradigm addresses these challenges by combining the robustness of symbolic AI with the flexibility of DL, enabling AI systems to reason, make decisions, and generalize knowledge from large datasets better. This paper reviews state-of-the-art DL models for IoT, identifies their limitations, and explores how neuro-symbolic methods can overcome them. It also discusses key challenges and research opportunities in enhancing AIoT reliability with neuro-symbolic approaches, including hard-coded symbolic AI, multimodal sensor data, biased interpretability, trading-off interpretability, and performance, complexity in integrating neural networks and symbolic AI, and ethical and societal challenges. © The Author(s) 2024.

关键词： Deep learning

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Dual Intent Enhanced Graph Neural Network for Session-based New Item Recommendation 23

Dual Intent Enhanced Graph Neural Network for Session-based ...

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2023 World Wide Web Conference, WWW 2023

作者： Jin, Di Wang, Luzhi Zheng, Yizhen Song, Guojie Jiang, Fei Li, Xiang Lin, Wei Pan, Shirui College of Intelligence and Computing Tianjin University China Department of Data Science and Ai Faculty of It Monash University Australia School of Intelligence Science and Technology Peking University China Meituan China Professional China Griffith University Australia

ISBN: (纸本)9781450394161

Recommender systems are essential to various fields, e.g., e-commerce, e-learning, and streaming media. At present, graph neural networks (GNNs) for session-based recommendations normally can only recommend items existing in users' historical sessions. As a result, these GNNs have difficulty recommending items that users have never interacted with (new items), which leads to a phenomenon of information cocoon. Therefore, it is necessary to recommend new items to users. As there is no interaction between new items and users, we cannot include new items when building session graphs for GNN session-based recommender systems. Thus, it is challenging to recommend new items for users when using GNN-based methods. We regard this challenge as "GNN Session-based New Item Recommendation (GSNIR)". To solve this problem, we propose a dual-intent enhanced graph neural network for it. Due to the fact that new items are not tied to historical sessions, the users' intent is difficult to predict. We design a dual-intent network to learn user intent from an attention mechanism and the distribution of historical data respectively, which can simulate users' decision-making process in interacting with a new item. To solve the challenge that new items cannot be learned by GNNs, inspired by zero-shot learning (ZSL), we infer the new item representation in GNN space by using their attributes. By outputting new item probabilities, which contain recommendation scores of the corresponding items, the new items with higher scores are recommended to users. Experiments on two representative real-world datasets show the superiority of our proposed method. The case study from the real-world verifies interpretability benefits brought by the dual-intent module and the new item reasoning module. © 2023 ACM.

关键词： Recommender systems

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