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IEEE Conference on Wireless Communications and Networking

作者： Nam H. Chu Diep N. Nguyen Dinh Thai Hoang Khoa T. Phan Eryk Dutkiewicz Dusit Niyato Tao Shu School of Electrical and Data Engineering University of Technology Sydney Australia School of Engineering and Mathematical Sciences La Trobe University Melbourne Australia School of Computer Science and Engineering Nanyang Technological University Singapore Department of Computer Science and Software Engineering Auburn University USA

This work proposes a novel framework to dynamically and effectively manage and allocate different types of resources for Metaverse applications, which are forecasted to demand massive resources of various types that have never been seen before. Specifically, by studying functions of Metaverse applications, we first propose an effective solution to divide applications into groups, namely MetaInstances, where common functions can be shared among applications to enhance resource usage efficiency. Then, to capture the real-time, dynamic, and uncertain characteristics of request arrival and application departure processes, we develop a semi-Markov decision process-based framework and propose an intelligent algorithm that can gradually learn the optimal admission policy to maximize the revenue and resource usage efficiency for the Metaverse service provider and at the same time enhance the Quality-of-Service for Metaverse users. Extensive simulation results show that our proposed approach can achieve up to 120% greater revenue for the Metaverse service providers and up to 178.9% higher acceptance probability for Metaverse application requests than those of other baselines.

关键词： Deep learning Metaverse Heuristic algorithms Simulation Reinforcement learning Quality of service Dynamic scheduling

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Flowmind2Digital: The First Comprehensive Flowmind Recognition and Conversion Approach

arXiv

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arXiv 2024年

作者： Liu, Huanyu Cai, Jianfeng Zhang, Tingjia Li, Hongsheng Wang, Siyuan Zhu, Guangming Shah, Syed Afaq Ali Bennamoun, Mohammed Zhang, Liang School of Artificial Intelligence Xidian University China School of Computer Science and Technology Xidian University China School of Science Centre for AI and Machine Learning ECU School of Physics Maths and Computing Computer Science and Software Engineering UWA

Flowcharts and mind maps, collectively referred to as flowmind, play an important role in our daily, and many companies have developed dedicated tools. Hand-drawn flowminds offer significant advantages for real-time and collaborative communication. However, there is an increasing demand to convert them into a digital format for further processing. Automated conversion methods are crucial in addressing the challenges associated with manual conversion, such as the cost of time and learning. Previous works have proposed diverse sketch recognition methods. However, these methods face significant limitations in practical situations. Firstly, most methods are designed for specific fields, making it challenging to extend their usage to other fields. Additionally, none of these methods address the critical step of digital conversion after recognition, which is essential to users. Moreover, existing datasets exhibit significant biases relative to actual data, hindering the methods’ generalization ability. Our paper proposes the Flowmind2digital method and hdFlowmind dataset to address the aforementioned challenges. Flowmind2digital is the first comprehensive recognition and conversion method for flowminds, utilizing a neural network architecture and keypoint detection technology to enhance overall recognition accuracy. Our hdFlowmind dataset consists of 1,776 hand-drawn and manually annotated flowminds, covering 22 scenarios and surpassing existing datasets in size. Our experiments showcase the effectiveness of our method, with an accuracy rate of 87.3% on the hdFlowmind dataset, surpassing the previous state-of-the-art work by 11.9%. Additionally, our dataset demonstrates effectiveness, with a 2.9% increase in accuracy after pre-training and fine-tuning on Handwritten-diagram-dataset. We also highlight the importance of simple graphics for sketch recognition, which can improve accuracy by 9.3%. Copyright © 2024, The Authors. All rights reserved.

关键词： Image recognition

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Customer Churn Prediction Framework of Inclusive Finance Based on Blockchain Smart Contract

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computer Systems Science & engineering 2023年第10期47卷 1-17页

作者： Fang Yu Wenbin Bi Ning Cao Hongjun Li Russell Higgs College of Information Engineering Qingdao Binhai UniversityQingdao266555China School of Computer and Software Dalian Neusoft University of InformationDalian116023China College of Information Engineering Shandong Vocational and Technical University of International StudiesRizhao276826China School of Intergrated Circuit Wuxi Vocational College of Science and TechnologyWuxi214028China School of Mathematics and Statistics University College DublinDublinIreland

In view of the fact that the prediction effect of influential financial customer churn in the Internet of Things environment is difficult to achieve the expectation,at the smart contract level of the blockchain,a customer churn prediction framework based on situational awareness and integrating customer attributes,the impact of project hotspots on customer interests,and customer satisfaction with the project has been *** framework introduces the background factors in the financial customer environment,and further discusses the relationship between customers,the background of customers and the characteristics of pre-lost *** improved Singular Value Decomposition(SVD)algorithm and the time decay function are used to optimize the search and analysis of the characteristics of pre-lost customers,and the key index combination is screened to obtain the data of potential lost *** framework will change with time according to the customer’s interest,adding the time factor to the customer churn prediction,and improving the dimensionality reduction and prediction generalization ability in feature *** regression,naive Bayes and decision tree are used to establish a prediction model in the experiment,and it is compared with the financial customer churn prediction framework under situational *** prediction results of the framework are evaluated from four aspects:accuracy,accuracy,recall rate and *** experimental results show that the context-aware customer churn prediction framework can be effectively applied to predict customer churn trends,so as to obtain potential customer data with high churn probability,and then these data can be transmitted to the company’s customer service department in time,so as to improve customer churn rate and customer loyalty through accurate service.

关键词： Contextual awareness customer churn prediction framework dimensionality reduction generalization ability

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Incomplete Multi-View Representation Learning Through Anchor Graph-Based GCN and Information Bottleneck

Incomplete Multi-View Representation Learning Through Anchor...

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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Zhenjiao Liu Xiao Wang Xiaodi Huang Guanlin Li Ke Sun Zhikui Chen School of Software Technology Dalian University of Technology Dalian China Samovar Telecom SudParis Institut Polytechnique de Paris Palaiseau France School of Computing Mathematics and Engineering Charles Sturt University Albury Australia School of Computer Science and Technology Dalian University of Technology

Real-world data often contain incomplete views with varying degrees of missing information. While there are existing methods for learning representations from such data, effectively utilizing all incomplete view data and ensuring robustness to different levels of completeness remains a challenging task. To address this problem, we propose a novel framework named IMRL-AGI. IMRL-AGI combines the anchor graph-based Graph Convolutional Network (GCN) and information bottleneck. Specifically, the framework starts by constructing an anchor graph to effectively captures the nonlinear information between instances. Next, an anchor graph-based GCN is designed to extract feature information from various views. IMRL-AGI maximizes the mutual information between the views obtained by the common representation and the anchor-graph-based GCN, ensuring the accurate extraction of view information. Furthermore, the minimization of mutual information is applied to promote diversity and reduce redundancy in the multi-view representation. Extensive experiments are conducted on several real-world datasets, and the results demonstrate the superiority of IMRL-AGI.

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Research on Improved Quick Sort Algorithm with Duplicate Value Handling

Research on Improved Quick Sort Algorithm with Duplicate Val...

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International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud) (I-SMAC)

作者： Hemin Amit Yadav Asif Khan Abhishek Pratap Sah College of Computer and Software Chengdu Neusoft University Chengdu China College of Engineering IT and Environment Charles Darwin University NT Australia Department of Computer Application Integral University Lucknow India School of Management Science and Engineering University of Electronic Science and Technology of China Chengdu China

ISBN: (数字)9798350376425

ISBN: (纸本)9798350376432

An improved algorithm based on Quick Sort algorithm research method is proposed to deal with prevailing duplicate values in the sorting of data. The duplicate values are specially processed, which effectively reduces the number of division and greatly reduces the number of recursions. The experimental results show that the time efficiency is improved by more than 50% when dealing with large data than the traditional Quick Sort algorithm without reducing the space efficiency.

关键词： Distributed processing Multicore processing Big Data Stability analysis Hardware Distributed computing Optimization Sorting

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Optimizing Endotracheal Suctioning Classification: Leveraging Prompt engineering in Machine Learning for Feature Selection

Optimizing Endotracheal Suctioning Classification: Leveragin...

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Activity and Behavior Computing (ABC), International Conference on

作者： Mahera Roksana Islam Anik Mahmud Ferdous Shahera Hossain Md Atiqur Rahman Ahad Fady Alnajjar Dept. of Electrical and Electronic Engineering University of Dhaka Dhaka Bangladesh Dept. of Computer Science and Digital Technologies University of East London London UK Dept. of Computer Science and Software Engineering College of Information Technology UAE University Al Ain UAE

ISBN: (数字)9798350375503

ISBN: (纸本)9798350375510

In a world with an overgrowing elderly population, there exists a critical need for a greater number of skilled individuals in the nursing industry. AI-based systems can be useful, compared to traditional ones with require in-person assistance, to accurately identify nursing activities and assess the nursing trainees to help them become proficient. This paper addresses classifying activities in one such procedure, endotracheal suctioning, using skeletal keypoint data of the subject performing the procedure. A multi-step structured prompt engineering method was established and utilized on several LLMs to select or calculate key features from the data. Then the features are passed onto several tuned machine learning models to obtain results. A tuned XGBoost prevailed across all models, achieving 90% accuracy on the validation set.

关键词： Training Accuracy Computational modeling Machine learning Medical services Predictive models Feature extraction

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Identification of Data Breaches from Public Forums 1

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14th International Conference on Innovative Security Solutions for Information technology and Communications, SecITC 2021

作者： Adnan, Md. Akhtaruzzaman Younus, Atika Kawser, Md. Harun Al Adhikary, Natasha Habib, Ahsan Haque, Rakib Ul Department of Computer Science and Engineering University of Asia Pacific Dhaka1205 Bangladesh Indetechs Software Limited House: 31 Road: 20 Block: K Banani Dhaka1212 Bangladesh School of Computer Science & Technology University of Chinese Academy of Sciences Shijingshan Beijing100049 China

ISBN: (数字)9783031175107

ISBN: (纸本)9783031175091

Adversaries initiate their cyberattacks towards different entities such as healthcare or business institutes, and a successful attack causes data breaches. They publish their success stories in public forums for ranking purposes. The victim entities can be informed early about the data breach event if these forums are analyzed properly. Though few studies already focused on this sector, their data sets and codes are not public. Most importantly, the sources of their data sets do not exist today, which makes their novelty unclear and unreliable. To address and handle the above concerns, this study reinvestigates this domain with Machine Learning, Ensemble Learning, and Deep Learning. A web crawler is developed for downloading the dataset from the public forum of Nulled website. Feature extraction is done using TF-IDF and GloVe. Performance analysis showed that SVM achieved at most 90.80% accuracy with linear kernel. Implementations are published with a GitHub link. © 2022, Springer Nature Switzerland AG.

关键词： Web crawler

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A Parallel Change Detection Method for Spatiotemporally Multi-Temporal SAR Image Based on Q-Learning and Wavelet 14

A Parallel Change Detection Method for Spatiotemporally Mult...

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14th International Conference on Digital Image Processing, ICDIP 2022

作者： Peng, Jinxi Su, Yuanqi Xue, Xiaorong Song, Donghong Xie, Zhengbo Xue, Xiaoyong Fan, Wenlong Department of Computer Science and Technology Xían JiaoTong University Shaanxi Xi'an710049 China Department of software Engineering South China institute of software Engineering Guangdong Guangzhou510990 China Liaonin Jinzhou121001 China The Middle Training School of Zhenan County Zhen'an Shaanxi 711500 China The Middle School of Luonan County Luonan Shaanxi 726100 China

ISBN: (纸本)9781510657564

In order to make full use of multi-source remote sensing image resources in the application of remote sensing image change detection, a parallel computing for coarse-grained computing data is a very important choice. For the large-scale explosive growth of remote sensing image change which is contained coherent speckles detection sensor data, it’s the amount of data calculation is large and time-consuming. With the development of modern remote sensing big data and Artificial Intelligence (AI), however, it is difficult for traditional methods to solve an efficient algorithm for change detection in synthetic aperture radar application. In connection with acquiring SAR (Polarimetric Synthetic Aperture Radar) Image to accurately obtain an effectively computing power. Looking for data processing and task computing as the basis for parallelization, we propose a parallel change detection method for multi-temporal SAR images based on wavelet transform, and comprehensively apply the Q-learning model of parallel computing with intelligent processing. Firstly, according to the statistical features of SAR images and the semantics of Convolutional Neural Network Pixel (CNN-Pixel) analysis for an efficient change detection method based on Q-Learning semantic analysis and wavelet transform is proposed. Secondly, on the basis of Pixel-Data is achieved the accuracy of change detection, probabilistic and statistical conjugate multi-distribution function features, which is pixels and tasks are pre-processed, pixel-space data calculation of Gaussian mixture model and conjugate gradient transform task model for parallel calculation. Thirdly, the change-sequence data with obstacles at the time series model of Q-learning in the changing target object data on which the surrounding path is an obstacle to achieving that image-pixel sequence data of conjugate gradient calculation data and threshold iteratively optimizes the feature extraction of the changing area, the multi-sample training se

关键词： Change detection

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High-Fidelity and High-Efficiency Talking Portrait Synthesis With Detail-Aware Neural Radiance Fields

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IEEE Transactions on Visualization and computer Graphics 2024年 PP卷 PP-PP页

作者： Wang, Muyu Zhao, Sanyuan Dong, Xingping Shen, Jianbing Wuhan University School of Computer Science National Engineering Research Center for Multimedia Software Institute of Artificial Intelligence Hubei Key Laboratory of Multimedia and Network Communication Engineering Wuhan430072 China Beijing Institute of Technology School of Computer Science Beijing China University of Macau State Key Laboratory of Internet of Things for Smart City Department of Computer and Information Science China

In this paper, we propose a novel rendering framework based on neural radiance fields (NeRF) named HH-NeRF that can generate high-resolution audio-driven talking portrait videos with high fidelity and fast rendering. Specifically, our framework includes a detail-aware NeRF module and an efficient conditional super-resolution module. Firstly, a detail-aware NeRF is proposed to efficiently generate a high-fidelity low-resolution talking head, by using the encoded volume density estimation and audio-eye-aware color calculation. This module can capture natural eye blinks and high-frequency details, and maintain a similar rendering time as previous fast methods. Secondly, we present an efficient conditional super-resolution module on the dynamic scene to directly generate the high-resolution portrait with our low-resolution head. Incorporated with the prior information, such as depth map and audio features, our new proposed efficient conditional super resolution module can adopt a lightweight network to efficiently generate realistic and distinct high-resolution videos. Extensive experiments demonstrate that our method can generate more distinct and fidelity talking portraits on high resolution (900 × 900) videos compared to state-of-the-art methods. Our code is available at https://***/muyuWang/HHNeRF. © 1995-2012 IEEE.

关键词： Rendering (computer graphics)

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A face recognition algorithm based on feature fusion

A face recognition algorithm based on feature fusion

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作者： Zhang, Jiwei Yan, Xiaodan Cheng, Zelei Shen, Xueqi School of Software Engineering Beijing University of Posts and Telecommunications Beijing China School of Cyberspace Security Beijing University of Posts and Telecommunications Beijing China Department of Computer and Information Technology Purdue University West LafayetteIN United States

In the process of building a smart city, face recognition can be applied to the transformation of enterprises, communities, and parks. The combination of building security system and face recognition technology can improve the security experience of enterprises and citizens through the solution of hardware and software integration. Face recognition is still facing the challenges of illumination, occlusion, and attitude change in the actual application process. In addition, the end-to-end convolutional neural networks (CNN) seldom make use of the hierarchical feature of the network. So, we propose a hierarchy feature fusion method for face recognition, which uses supervisory information to learn shallow and deep facial features. The features are fused to enhance the recognition accuracy of face recognition against illumination and occlusion. The method is applied to the transformation of the visual geometry group network and Lightened CNN. The face recognition experiments are carried out using the hierarchy network. Our method has achieved good recognition results in the labeled faces in the wild (LFW) and AR face databases. © 2020 John Wiley & Sons, Ltd.

关键词： Face recognition

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