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Optimizing control strategies for monkeypox through mathematical modeling

Partial Differential Equations in Applied Mathematics 2024年 12卷

作者： Baroudi, Mohamed Smouni, Imane Gourram, Hicham Labzai, Abderrahim Belam, Mohamed Laboratory LMACS Sultan Moulay Slimane University MATIC Research Team: Applied Mathematics and Information and Communication Technologie Department of Mathematics and Computer Science Khouribga Polydisciplinary Faculty Morocco Laboratory of Analysis Modeling and Simulation Department of Mathematics and Computer Science Faculty of Sciences Ben M'sik Hassan II University of Casablanca Morocco

Monkeypox is a zoonotic viral disease similar to smallpox, has emerged as a major global health concern following the COVID-19 pandemic. This study presents a novel mathematical model aimed at analyzing various epidemiological factors, particularly the less-explored transmission from humans to monkeys, where both species act as carriers. Our approach integrates comprehensive awareness campaigns, strict security measures, and targeted health interventions to limit transmission between hosts, with the goal of reducing human infections and eliminating the virus among animal populations. The model utilizes the continuous-time Pontryagin maximum principle to determine and apply optimal control strategies, with iterative simulations conducted in Matlab. Our results, derived from these simulations, show that implementing all proposed preventative strategies—such as public awareness efforts, isolation of infected monkeys, and vaccination—simultaneously is the most effective method to control the virus's spread. We observed a significant reduction in both human and animal infections when these strategies were combined. The study's conclusions provide important insights into the transmission dynamics of monkeypox, highlighting the critical role of multifaceted intervention strategies in controlling outbreaks. These findings are expected to support more effective public health management and contribute to the global effort to contain and ultimately eradicate monkeypox. © 2024 The Authors

关键词： Control optimal Matlab Monkeypox virus Pontryagin's maximum principle

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Recognition of Combustion Conditions in MSWI Process based on Hierarchical-Split ResNet

Recognition of Combustion Conditions in MSWI Process based o...

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Chinese Control and Decision Conference, CCDC

作者： Jian Zhang Rongcheng Sun Gang Yu Jianbo Yu Jian Tang School of Computer Science Nanjing University of Information Science & Technology Nanjing China NUIST-TianChang Research Institute Nanjing University of Information Science & Technology Nanjing China State & Beijing Key Laboratory of Process Automation in Mining & Metallurgy Beijing China Faculty of Information Technology Beijing University of Technology Beijing China

ISBN: (数字)9798350387780

ISBN: (纸本)9798350387797

Municipal solid waste incineration (MSWI) has significant advantages in the treatment of municipal solid waste, however, the instability of the solid waste combustion state can lead to a reduction in the operational efficiency of the whole system. In addition, the judgement of the MSWI state mainly relies on the operators' own experience to control the combustion, leading to the identification results with substantially subjective and arbitrary. In order to address these challenging problems, a Hierarchical-Split residual neural network is proposed for MSWI process combustion state recognition, which is modeled by collecting combustion images, combining the experience of field experts and the incinerator grate structure, and classifying the combustion state into four categories: normal burning, partial burning, channelling burning, and smothering. Aiming at the problem of incomplete image information extracted by the traditional single-scale convolution, the Hierarchical-Split feature extraction module is utilized not only to enhance the ability of the model to extract feature information, but also to improve the recognition accuracy of the model. Finally, the proposed method is validated by taking the flame data of a municipal waste treatment plant in Beijing as an example. The experimental results show that the method effectively improves the generalization and robustness, and has good recognition accuracy.

关键词： Waste management Waste materials Accuracy Image recognition Incineration Fires Feature extraction

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GenFace: A Large-Scale Fine-Grained Face Forgery Benchmark and Cross Appearance-Edge Learning

arXiv

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arXiv 2024年

作者： Zhang, Yaning Yu, Zitong Wang, Tianyi Huang, Xiaobin Shen, Linlin Gao, Zan Ren, Jianfeng Computer Vision Institute College of Computer Science and Software Engineering Shenzhen University Shenzhen518060 China School of Computing and Information Technology Great Bay University Dongguan523000 China National Engineering Laboratory for Big Data System Computing Technology Shenzhen University Shenzhen518060 China Nanyang Technological University 50 Nanyang Ave Block N 4 639798 Singapore Shenzhen Institute of Artificial Intelligence and Robotics for Society Shenzhen518129 China Guangdong Key Laboratory of Intelligent Information Processing Shenzhen University China Jinan250014 China Key Laboratory of Computer Vision and System Ministry of Education Tianjin University of Technology Tianjin300384 China School of Computer Science University of Nottingham Ningbo China

The rapid advancement of photorealistic generators has reached a critical juncture where the discrepancy between authentic and manipulated images is increasingly indistinguishable. Thus, benchmarking and advancing techniques detecting digital manipulation become an urgent issue. Although there have been a number of publicly available face forgery datasets, the forgery faces are mostly generated using GAN-based synthesis technology, which does not involve the most recent technologies like diffusion. The diversity and quality of images generated by diffusion models have been significantly improved and thus a much more challenging face forgery dataset shall be used to evaluate SOTA forgery detection literature. In this paper, we propose a large-scale, diverse, and fine-grained high-fidelity dataset, namely GenFace, to facilitate the advancement of deepfake detection, which contains a large number of forgery faces generated by advanced generators such as the diffusion-based model and more detailed labels about the manipulation approaches and adopted generators. In addition to evaluating SOTA approaches on our benchmark, we design an innovative Cross Appearance-Edge Learning (CAEL) detector to capture multi-grained appearance and edge global representations, and detect discriminative and general forgery traces. Moreover, we devise an Appearance-Edge Cross-Attention (AECA) module to explore the various integrations across two domains. Extensive experiment results and visualizations show that our detection model outperforms the state of the arts on different settings like cross-generator, cross-forgery, and cross-dataset evaluations. Code and datasets will be available at https://***/Jenine-321/GenFace. Copyright © 2024, The Authors. All rights reserved.

关键词： Image enhancement

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Lightweight Deep Residual Network for Motorcyclist Behavior Recognition Using Inertial Sensors

Lightweight Deep Residual Network for Motorcyclist Behavior ...

引用

International Conference on information Technology (INCIT)

作者： Narit Hnoohom Sakorn Mekruksavanich Anuchit Jitpattanakul Image Information and Intelligence Laboratory Department of Computer Engineering Faculty of Engineering Mahidol University Nakhon Pathom Thailand Department of Computer Engineering School of Information and Communication Technology University of Phayao Phayao Thailand Intelligent and Nonlinear Dynamic Innovations Research Center Department of Mathematics Faculty of Applied Science King Mongkut’s University of Technology North Bangkok Thailand

ISBN: (数字)9798350366303

ISBN: (纸本)9798350366310

Motorcyclist behavior recognition (MBR) is a significant factor in improving road safety and preventing accidents. Inertial sensors, such as accelerometers, gyroscopes, and magnetometers, have shown promising potential for capturing the dynamic behavior of motorcyclists. However, existing approaches often require complex models and significant computational resources, limiting their deployment in real-world scenarios. This study presents a MotoNeXt architecture, which is a light deep residual network for correctly identifying the behavior of motorcyclists using data from inertial sensors. Our approach leverages the power of deep learning while maintaining a compact model size, making it suitable for resource-constrained environments. The proposed network incorporates multi-kernel residual blocks to facilitate the learning of hierarchical features and improve gradient flow during training. We evaluate our method on a publicly available benchmark dataset, MB-IMU, collected from multiple riders under various riding conditions. Experimentation has shown that our lightweight model achieves cutting-edge performance, outperforming a variety of existing techniques. In Scenario I, using accelerometer, gyroscope, and magnetometer data, MotoNeXt achieved an accuracy result of 78.90088% along with an F1-score of 81.56738%. According to Scenario II, which included additional sensor data such as VelInc, OriInc, and Euler angles, MotoNeXt further improved its performance, achieving an accuracy result of 83.29735% along with an F1-score of 84.95352%. The proposed approach offers a practical solution for real-time MBR, enabling the development of intelligent transportation systems and rider assistance technologies. Our experiments highlight the effectiveness of lightweight deep learning models in extracting meaningful patterns from inertial sensor data. This opens up opportunities for further investigation in this field.

关键词： Deep learning Accelerometers Training Accuracy Inertial sensors Magnetometers Computational modeling Transportation Gyroscopes Residual neural networks

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PLIP: Language-Image Pre-training for Person Representation Learning

arXiv

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arXiv 2023年

作者： Zuo, Jialong Hong, Jiahao Zhang, Feng Yu, Changqian Zhou, Hanyu Gao, Changxin Sang, Nong Wang, Jingdong National Key Laboratory of Multispectral Information Intelligent Processing Technology School of Artificial Intelligence and Automation Huazhong University of Science and Technology China Skywork AI Department of Computer Vision Baidu Inc China

Language-image pre-training is an effective technique for learning powerful representations in general domains. However, when directly turning to person representation learning, these general pre-training methods suffer from unsatisfactory performance. The reason is that they neglect critical person-related characteristics, i.e., fine-grained attributes and identities. To address this issue, we propose a novel language-image pre-training framework for person representation learning, termed PLIP. Specifically, we elaborately design three pretext tasks: 1) Text-guided Image Colorization, aims to establish the correspondence between the person-related image regions and the fine-grained color-part textual phrases. 2) Image-guided Attributes Prediction, aims to mine fine-grained attribute information of the person body in the image;and 3) Identity-based vision-Language Contrast, aims to correlate the cross-modal representations at the identity level rather than the instance level. Moreover, to implement our pre-train framework, we construct a large-scale person dataset with image-text pairs named SYNTH-PEDES by automatically generating textual annotations. We pre-train PLIP on SYNTH-PEDES and evaluate our models by spanning downstream person-centric tasks. PLIP not only significantly improves existing methods on all these tasks, but also shows great ability in the zero-shot and domain generalization settings. © 2023, CC BY-NC-SA.

关键词： Large datasets

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Fog Computing in the Age of Big Healthcare Data: Powering the Medical Internet of Things 1st

Fog Computing in the Age of Big Healthcare Data: Powering th...

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1st International Conference of Advanced Computing and Informatics, ICACIN 2020

作者： Khaloufi, Hayat Abouelmehdi, Karim Beni-Hssane, Abederrahim Department of Computer Science LAROSERI Laboratory Faculty of Science El Jadida Chouaib Doukkali University El Jadida Morocco Laboratory of Applied Mathematics and Information System Faculty Polydisciplinary of Nador Mohamed First University Nador Morocco

ISBN: (纸本)9789811560477

Today’s global marketplace for Internet of Things (IoT) remains fiercely competitive within the healthcare sector. This can be attributed to many exciting achievements. It is expected to have a positive influence on all aspects of healthcare such as reducing prevalence and incidence of chronic conditions, improving the patient treatment and lowering in per capita healthcare costs, resulting from the forecasted increases in the adoption of remote monitoring devices. These smart devices along with wearables and Internet-connected sensors are expected to generate a great amount of data every second. However, the present model used to handle these unprecedented data named as cloud computing becomes inadequate. The solution must be seen in fog computing. The strong need of this model was mainly driven by a growing demand for real-time analysis and low latency. Motivated from these facts, this paper defines fog computing for IoT-driven smart healthcare while comparing it with cloud computing. It also presents an architecture of fog-based healthcare system containing three layers: edge, fog, and cloud. Finally, this paper highlights opportunities and top challenges facing smart healthcare organizations in the coming years. © 2021, The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Fog computing

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Federated Learning and Convolutional Neural Networks for Intrusion Detection Systems

Federated Learning and Convolutional Neural Networks for Int...

引用

International Conference on Pattern Analysis and Intelligent Systems (PAIS)

作者： Malika Abid Mohammed Kamel Benkaddour Mohamed Benouis Computer Science Department Faculty of Sciences and Technology Artificial Intelligence and Information Technology Laboratory (LINATI) University of Kasdi Merbah Ouargla Algeria Human-Centered Artificial Intelligence University of Augsburg Augsburg Germany

ISBN: (数字)9798350350265

ISBN: (纸本)9798350350272

Recently, there has been a growing focus on network traffic threats due to the proliferation of mobile devices and the expanding Internet of Things (IoT). The centralised anomaly detection approach implies sharing the user’s data with a cloud server, which mostly exposes the user to the data breach issue. Given the above issues, this paper proposes a federated learning intrusion detection system (FedIDS) to improve the performance of intrusion detection while protecting user privacy. Motivated by recent exploits into deep learning for intrusion detection, we propose a convolutional neural network (CNN) model to learn and train the record data collected from independent and non-independent identically distributed (Non-IID, IID)data. This work conducts experiments on the NSL-KDD Data set for network intrusion detection. Experimental results show that FedIDS has achieved a competitive advantage over state-of-the-art centralised machine learning methods.

关键词： Performance evaluation Analytical models Privacy Federated learning Intrusion detection Telecommunication traffic Convolutional neural networks

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Code Refactoring for Software Reusability: An Experimental Study

Code Refactoring for Software Reusability: An Experimental S...

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Emerging Smart Technologies and Applications (eSmarTA), International Conference on

作者： Abdullah Almogahed Hairulnizam Mahdin Mazidah Mat Rejab Abdulwadood Alawadhi Samera Obaid Barraood Manal Othman Omar Al-Jamili Abdulwahab Ali Almazroi Shazlyn Milleana Shaharudin Faculty of Computer Science and Information Technology Universiti Tun Hussein Onn Malaysia Parit Raja Johor Malaysia InterNetWorks Research Laboratory School of Computing Universiti Utara Malaysia Sintok Malaysia College of Computers and Information Technology Hadhramout University Hadhramout Yemen Computer Science Department College of Applied Science Taiz University Taiz Yemen Faculty of Engineering and Computing University of Science and Technology Yemen Department of Information Technology College of Computing and Information Technology at Khulais University of Jeddah Jeddah Saudi Arabia Faculty of Science and Mathematics Universiti Pendidikan Sultan Idris Tanjong Malim Malaysia

ISBN: (数字)9798350354133

ISBN: (纸本)9798350354140

Refactoring plays a critical role in enhancing the quality of software systems, enabling them to accommodate growing user bases, feature sets, and performance demands. Software reusability is an essential aspect of software quality. It is crucial in modern software development as it offers several benefits, including increased productivity, improved code quality, and cost-effectiveness. However, there is a lack of studies investigating how specific refactoring techniques affect reusability. Therefore, this study aims to explore the effects of code refactoring on software reusability. This study was conducted in four stages: selecting refactoring techniques, selecting a large dataset, selecting software metrics to measure the reusability, and applying the refactoring techniques. The five most common refactoring techniques (Extract Interface, Encapsulate Field, Extract Class, Inline Class, and Inline Method) used in practices were selected and applied in the jEdit dataset. The refactoring techniques were carried out 338 times through five experiments in the jEdit dataset. The finding reveals that Extract Interface, Encapsulate Field, and Extract Class significantly improve the code reusability while Inline Class and Inline Method impair it. Developers and software architects can use this knowledge to make well-informed judgments about refactoring techniques and develop more robust and reusable software systems.

关键词： Productivity Industries Codes Software quality Software systems Maintenance Software measurement

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Kepler-aSI: Semantic Annotation for Tabular Data

Kepler-aSI: Semantic Annotation for Tabular Data

引用

2024 Semantic Web Challenge on Tabular Data to Knowledge Graph Matching, SemTab 2024

作者： Baazouzi, Wiem Kachroudi, Marouen Faiz, Sami RIADI Laboratory National School of Computer Science University of Manouba Manouba2010 Tunisia ESPRIT School of Engineering El Ghazala 2080 Tunisia University of Tunis El Manar Faculty of Sciences of Tunis LR11ES14 Tunis2092 Tunisia University of Tunis El Manar National School of Engineers of Tunis Laboratory of Remote Sensing and Spatial Reference Information Systems 99/UR/11-11 Tunis2092 Tunisia

The identification of semantic concepts in tabular data is crucial for numerous applications, including data integration, cleaning, retrieval, feature engineering, and model development in machine learning. Recently, various studies have introduced methods using supervised learning or heuristic models to annotate semantic types. However, these approaches have limitations, making it difficult for them to generalize to a wide range of concepts or examples. Additionally, many neural network-based methods struggle with scalability, and the majority of the existing techniques do not perform well with numerical data. We present Kepler-aSI, a column-to-concept mapping technique that employs a maximum likelihood estimation approach through sets. This method effectively leverages large amounts of publicly available table data, even in the presence of some noise. We showcase the effectiveness of Kepler-aSI in the Semtab2024 challenge. © 2024 Copyright for this paper by its authors.

关键词： Self-supervised learning

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GeoDTR+: Toward Generic Cross-View Geolocalization via Geometric Disentanglement

arXiv

引用

arXiv 2023年

作者： Zhang, Xiaohan Li, Xingyu Sultani, Waqas Chen, Chen Wshah, Safwan Department of Computer Science and Vermont Complex Systems Center University of Vermont Burlington United States Lin Gang Laboratory Shanghai China Intelligent Machine Lab Information Technology University Pakistan Center for Research in Computer Vision University of Central Florida United States

Cross-View Geo-Localization (CVGL) estimates the location of a ground image by matching it to a geo-tagged aerial image in a database. Recent works achieve outstanding progress on CVGL benchmarks. However, existing methods still suffer from poor performance in cross-area evaluation, in which the training and testing data are captured from completely distinct areas. We attribute this deficiency to the lack of ability to extract the geometric layout of visual features and models' overfitting to low-level details. Our preliminary work [1] introduced a Geometric Layout Extractor (GLE) to capture the geometric layout from input features. However, the previous GLE does not fully exploit information in the input feature. In this work, we propose GeoDTR+ with an enhanced GLE module that better models the correlations among visual features. To fully explore the LS techniques from our preliminary work, we further propose Contrastive Hard Samples Generation (CHSG) to facilitate model training. Extensive experiments show that GeoDTR+ achieves state-of-the-art (SOTA) results in cross-area evaluation on CVUSA [2], CVACT [3], and VIGOR [4] by a large margin (16.44%, 22.71%, and 13.66% without polar transformation) while keeping the same-area performance comparable to existing SOTA. Moreover, we provide detailed analyses of GeoDTR+. Our code will be available at https://***/vail-uvm/geodtr_plus. Copyright © 2023, The Authors. All rights reserved.

关键词： Antenna grounds

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