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Model Comparisons: XNet Outperforms KAN

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arXiv 2024年

作者： Li, Xin Xia, Zhihong Zheng, Xiaotao Department of Computer Science Northwestern University EvanstonIL United States Mathematical Modelling and Data Analytics Center Oxford Suzhou Centre for Advanced Research Suzhou China School of Natural Science Great Bay University Guangdong China Department of Mathematics Northwestern University EvanstonIL United States Department of Mathematics Soochow University Suzhou China

In the fields of computational mathematics and artificial intelligence, the need for precise data modeling is crucial, especially for predictive machine learning tasks. This paper explores further XNet, a novel algorithm that employs the complex-valued Cauchy integral formula, offering a superior network architecture that surpasses traditional Multi-Layer Perceptrons (MLPs) and Kolmogorov-Arnold Networks (KANs). XNet significant improves speed and accuracy across various tasks in both low and high-dimensional spaces, redefining the scope of data-driven model development and providing substantial improvements over established time series models like LSTMs. © 2024, CC BY.

关键词： Choquet integral

VQC-COVID-NET: Vector Quantization Contrastive Learning for Covid-19 Image Base Classification

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VQC-COVID-NET: Vector Quantization Contrastive Learning for ...

National Foundation for science and Technology Development Conference on Information and Computer science (NICS)

作者： Linh Trinh Bach Ha Anh Tu Tran Data Science Department Vingroup Advanced Analytics Hanoi Vietnam Falculty of Applied Mathematics and Informatics Hanoi University of Science and Technology Hanoi Vietnam Falculty of Information Security Academy of Cryptography Techniques Hanoi Vietnam

Today, the COVID-19 epidemic has become extremely widespread. The first step in combating COVID-19 is identifying cases of infection. Real-time reverse transcriptase polymerase chain reaction is the most common method for identifying COVID (RT-PCR). This method, however, has been compromised by a time-consuming, laborious, and complex manual process. In addition to the RT-PCR test, screening computed tomography scan (CT) or X-ray images may be used to identify positive COVID-19 results, which could aid in the detection of COVID-19. Because of the continuing increase in new infections, the development of automated techniques for COVID-19 detection utilizing CT images is in high demand. This will aid in clinical diagnosis and alleviate the arduous task of image interpretation. Aggregating instances from various medical systems is highly advantageous for enlarging datasets for the development of machine learning techniques and the acquisition of robust, generalizable models. This study proposes a novel method for addressing distinct feature normalization in latent space due to cross-site domain shift in order to accurately execute COVID-19 identification using heterogeneous datasets with distribution disagreement. We propose using vector quantization to enhance the domain invariance of semantic embeddings in order to enhance classification performance on each dataset. We use two large, publicly accessible COVID-19 diagnostic CT scan datasets to develop and validate our proposed model. The experimental results demonstrate that our proposed method routinely outperforms state-of-the-art techniques on testing datasets. Public access to the implementation of our proposed method is available at https://***/khaclinh/VQC-COVID-NET.

关键词： COVID-19 Computed tomography Vector quantization Pulmonary diseases Soft sensors Semantics Task analysis

Wireless Power Transfer Technologies: State-Of-The-Art and Future Prospects

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Wireless Power Transfer Technologies: State-Of-The-Art and F...

Communication, Computing and Energy Efficient Technologies (I3CEET), International Conference on

作者： Vivek Justus S.Viveka M.Sumithra Purushothaman V V.Samuthira Pandi Desidi Narsimha Reddy Department of Computing Technologies School of Computing SRM Institute of Science and Technology Kattankulathur Tamil Nadu India Department of Computer Science and Engineering Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Chennai Tamilnadu India Department of Electronics and Communication Engineering Chennai Institute of Technology Chennai Centre for Advanced Wireless Integrated Technology Chennai Institute of Technology Chennai Tamil Nadu India Data Consultant (Data Governance Data Analytics: Enterprise Performance Management AI&ML) Soniks Consulting LLC Texas USA

ISBN: (数字)9798331541583

ISBN: (纸本)9798331541590

Wireless Power Transfer (WPT) technologies represent a transformative approach to energy transmission, offering advantages over traditional wired systems such as convenience, flexibility, and reduced maintenance. This paper presents a comprehensive review of current advancements and future prospects in WPT. The review encompasses three primary technologies: electromagnetic induction, magnetic resonance, and microwave transmission, each offering unique benefits and challenges. Recent developments have significantly enhanced WPT efficiency, extended transmission distances, and enabled integration with emerging technologies like Internet of Things (IoT) devices and electric vehicle (EV) charging infrastructure. Looking forward, the paper identifies several key research directions and challenges. These include improving efficiency through advanced materials and metamaterials, standardization efforts to promote interoperability and safety, and exploration of new applications such as wireless charging in smart cities and space-based applications. The potential impact of WPT on reducing reliance on fossil fuels and enabling sustainable energy practices underscores its importance in future energy landscapes. By synthesizing current knowledge and highlighting future opportunities, this study aims to guide researchers, engineers, and policymakers in accelerating the development and adoption of WPT technologies, paving the way for a moreefficient and interconnected energy ecosystem.

关键词： Costs Reviews Smart cities Magnetic resonance Wireless power transfer Standardization Telecommunication computing Smart grids Internet of Things Tuning

BIM: Improving Graph Neural Networks with Balanced Influence Maximization

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BIM: Improving Graph Neural Networks with Balanced Influence...

International Conference on data Engineering

作者： Wentao Zhang Xinyi Gao Ling Yang Meng Cao Ping Huang Jiulong Shan Hongzhi Yin Bin Cui Center for Machine Learning Research Peking University Institute of Advanced Algorithms Research Shanghai National Engineering Labratory for Big Data Analytics and Applications The University of Queensland Australia Key Lab of High Confidence Software Technologies Peking University Apple Inc. Institute of Computational Social Science Peking University Qingdao

ISBN: (数字)9798350317152

ISBN: (纸本)9798350317169

The imbalanced data classification problem has aroused lots of concerns from both academia and industry since data imbalance is a widespread phenomenon in many real-world scenarios. Although this problem has been well researched from the view of imbalanced class samples, we further argue that graph neural networks (GNNs) expose a unique source of imbalance from the influenced nodes of different classes of labeled nodes, i.e., labeled nodes are imbalanced in terms of the number of nodes they influenced during the influence propagation in GNNs. To tackle this previously unexplored influence-imbalance issue, we connect social influence maximization with the imbalanced node classification problem and propose balanced influence maximization (BIM). Specifically, BIM greedily assigns the pseudo label to the node which can maximize the number of influenced nodes in GNN training while making the influence of each class more balance. Experimental results on five public datasets demonstrate the effectiveness of our method in relieving the influence-imbalance issue. For example, when training a GCN with an imbalance ratio of 0.1, BIM significantly outperforms the most competitive baseline by 0.6% -9.8% in five public datasets in terms of the F1 score.

关键词： Training Industries data engineering Graph neural networks

All in One: Multi-Task Prompting for Graph Neural Networks

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arXiv 2023年

作者： Sun, Xiangguo Cheng, Hong Li, Jia Liu, Bo Guan, Jihong Department of Systems Engineering and Engineering Management Shun Hing Institute of Advanced Engineering The Chinese University of Hong Kong Hong Kong Data Science and Analytics Thrust The Hong Kong University of Science and Technology Guangzhou China School of Computer Science and Engineering Southeast University Purple Mountain Laboratories China Department of Computer Science and Technology Tongji University China

Recently, "pre-training and fine-tuning" has been adopted as a standard workflow for many graph tasks since it can take general graph knowledge to relieve the lack of graph annotations from each application. However, graph tasks with node level, edge level, and graph level are far diversified, making the pre-training pretext often incompatible with these multiple tasks. This gap may even cause a "negative transfer" to the specific application, leading to poor results. Inspired by the prompt learning in natural language processing (NLP), which has presented significant effectiveness in leveraging prior knowledge for various NLP tasks, we study the prompting topic for graphs with the motivation of filling the gap between pre-trained models and various graph tasks. In this paper, we propose a novel multi-task prompting method for graph models. Specifically, we first unify the format of graph prompts and language prompts with the prompt token, token structure, and inserting pattern. In this way, the prompting idea from NLP can be seamlessly introduced to the graph area. Then, to further narrow the gap between various graph tasks and state-of-the-art pre-training strategies, we further study the task space of various graph applications and reformulate downstream problems to the graph-level task. Afterward, we introduce meta-learning to efficiently learn a better initialization for the multi-task prompt of graphs so that our prompting framework can be more reliable and general for different tasks. We conduct extensive experiments, results from which demonstrate the superiority of our method. Copyright © 2023, The Authors. All rights reserved.

关键词： Graph neural networks

A Novel Method to Identify and Recover the Fault Nodes over 5G Wireless Sensor Network Environment

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A Novel Method to Identify and Recover the Fault Nodes over ...

2024 Asia Pacific Conference on Innovation in Technology, APCIT 2024

作者： Vijaya Vardan Reddy, S.P. Juliet, A. Hency Jayadurga, R. Sethu, S. Pavithra, K.N. Samuthira Pandi, V. R.M.K. Engineering College Department of Electronics and Communication Engineering Tamil Nadu Chennai India Saveetha Institute of Medical and Technical Sciences Department of Data Analytics Saveetha College of Liberal Arts and Sciences Saveetha University Tamil Nadu Chennai India Prince Shri Venkateshwara Padmavathy Engineering College Department of Master of Business Administration Tamil Nadu Chennai India Department of Computer Science and Engineering Tamilnadu Chennai India R. M. D. Engineering College Department of Science and Humanities Tamil Nadu Chennai India Chennai Institute of Technology Centre for Advanced Wireless Integrated Technology Tamil Nadu Chennai India

ISBN: (纸本)9798350361537

The significance of communication networks is growing in tandem with the proliferation of communication technologies. Present methods for maintaining 5G Wireless Sensor Networks are still restricted to routine maintenance and post-maintenance tasks. They lack a comprehensive function for monitoring the network's status, are unable to assess the network's health, and are difficult to maintain before the 5G-based WSNs seriously degrade. 5G Wireless Sensor Network faults can only be resolved by highly trained technicians due to low maintenance efficiency. As a result, errors cannot be detected or located promptly or accurately, leading to forced repairs that incur the expense of new network cables. First, the article lays forth the basics of network fault analysis. Then, it uses deep learning to simulate communication network problem diagnosis. Lastly, the experimental section compares various methodologies and analyses the findings of fault location. The results of the simulation demonstrate that the suggested approach mitigates the created model's flaws to a certain degree while simultaneously enhancing the network fault detection model's accuracy, universality, and robustness. A novel approach to autonomous placement, the Fault Node Recovery Protocol is described in this study. It is implemented in 5G mobile communications to detect faulty data according to wireless sensor network standards, and it is made to fix the problems with conventional techniques, such poor positioning precision and lengthy running time. The automated localization model for 5G mobile communication fault data, constructed using the suggested FNRP approach, is presented in this work. By comparing it to the standard Adhoc On-Demand Distance Vector Routing protocol, we can see how well the suggested system performs. © 2024 IEEE.

关键词： 5G mobile communication systems

Cauchy activation function and XNet

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arXiv 2024年

作者： Li, Xin Xia, Zhihong Zhang, Hongkun Department of Computer Science Northwestern University EvanstonIL United States Mathematical Modelling and Data Analytics Center Oxford Suzhou Centre for Advanced Research Suzhou China School of Natural Science Great Bay University Guangdong China Department of Mathematics Northwestern University EvanstonIL United States Department of Mathematics and Statistics University of Massachusetts Amherst AmherstMA United States

We have developed a novel activation function, named the Cauchy Activation Function. This function is derived from the Cauchy Integral Theorem in complex analysis and is specifically tailored for problems requiring high precision. This innovation has led to the creation of a new class of neural networks, which we call (Comple)XNet, or simply XNet. We will demonstrate that XNet is particularly effective for high-dimensional challenges such as image classification and solving Partial Differential Equations (PDEs). Our evaluations show that XNet significantly outperforms established benchmarks like MNIST and CIFAR-10 in computer vision, and offers substantial advantages over Physics-Informed Neural Networks (PINNs) in both low-dimensional and high-dimensional PDE scenarios. © 2024, CC BY.

关键词： Image classification

Industrial Internet of Things (IIoT): Transforming Industrial Operations through advanced Connectivity

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Industrial Internet of Things (IIoT): Transforming Industria...

Communication, Computing and Energy Efficient Technologies (I3CEET), International Conference on

作者： S. Viveka Vivek Justus Sangeetha J Desidi Narsimha Reddy V. Samuthira Pandi S. Banumathi Department of Computing Technologies School of Computing SRM Institute of Science and Technology Kattankulathur Tamil Nadu India Department of CSE S. A. Engineering College Chennai Tamil Nadu India Data Consultant (Data Governance Data Analytics: Enterprise Performance Management AI&ML) Soniks Consulting LLC Texas USA Centre for Advanced Wireless Integrated Technology Chennai Institute of Technology Chennai Tamil Nadu India Department of Computer Science Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Avadi Tamil Nadu India

ISBN: (数字)9798331541583

ISBN: (纸本)9798331541590

The Industrial Internet of Things (IIoT) seamlessly integrates operational technology with modern connectivity technologies, ushering in a new age in industrial operations. This paper examines IIoT's revolutionary potential in changing industrial landscapes through smart sensors, actuators, and intelligent devices that enable real-time data sharing and process automation. This paper uses case studies from numerous sectors to demonstrate how IIoT may improve operational efficiency, downtime, and decision-making. Manufacturing, energy, and logistics are discussed. The report also examines IIoT technologies like cloud, edge, and machine learning techniques. These help enterprises exploit enormous data sets for predictive maintenance, supply chain optimization, and product quality. The article discusses IIoT's risks and challenges, including data privacy, cybersecurity, and the need for skilled personnel. A detailed research shows IIoT's strategic importance in the fourth industrial revolution. It also discusses how industrial networking will enable smart and sustainable operations.

关键词： Actuators Supply chains Real-time systems Product design Telecommunication computing Quality assessment Personnel Intelligent sensors Industrial Internet of Things Predictive maintenance

Automating Speedrun Routing: Overview and Vision

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arXiv 2021年

作者： Groß, Matthias Zühlke, Dietlind Naujoks, Boris Advanced Media Institute TH Köln Germany Institute for Data Science Engineering and Analytics TH Köln Germany

Speedrunning in general means to play a video game fast, i.e. using all means at one's disposal to achieve a given goal in the least amount of time possible. To do so, a speedrun must be planned in advance, or routed, as referred to by the community. This paper focuses on discovering challenges and defining models needed when trying to approach the problem of routing algorithmically. To do so, this paper is split in two parts. The first part provides an overview of relevant speedrunning literature, extracting vital information and formulating criticism. Important categorizations are pointed out and a nomenclature is built to support professional discussion. The second part of this paper then refers to the actual speedrun routing optimization problem. Different concepts of graph representations are presented and their potential is discussed. Visions both for problem modeling as well as solving are presented and assessed regarding suitability and expected challenges. Finally, a first assessment of the applicability of existing optimization methods to the defined problem is made, including metaheuristics/EA and Deep Learning methods. © 2021, CC BY.

关键词： Heuristic algorithms