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Hierarchical Reverse Games for the Resource Ecosystem in Cloud-Edge Computing

IEEE Transactions on Consumer Electronics 2024年

作者： Pei, Manlin Wang, Chenyu Wang, Xia Zheng, Zhigao Huang, Jianhui Wang, Shengling Guo, Yufei School of Computer and Information Technology Beijing Jiaotong University Beijing China Department of Computer Science Georgia State University United States Beijing University of Technology School of Statistics and Data Science Faculty of Science Beijing China Institute of Artificial Intelligence School of Computer Science National Engineering Research Center for Multimedia Software Hubei Key Laboratory of Multimedia and Network Communication Engineering Hubei Luojia Laboratory Wuhan University Wuhan China Chinese Academy of Sciences Institute of Computing Technology Beijing China Beijing Normal University School of Artificial Intelligence Beijing China George Washington University Department of Statistics United States

Edge computing has emerged as a killer technology for a hyper-connected world due to its distributed architecture and customer-proximity property. Combined edge nodes with the cloud data center, a cloud-edge computing paradigm is formed, whose resource ecosystem calls for competitive pricing and optimal resource allocation. However, it is not feasible to realize global information-based optimization since some information is kept private to their owners. To solve this challenge, this paper develops the user-independent hierarchical reverse game and the user-assisted hierarchical reverse game to depict the relationship among the resource provider, the resource tenant, and the resource user. The aim is to seek the optimal strategies for players without revealing any private information. The common trait of both games is to force the players to report the optimal strategies based on their actual private information. This salient trait can produce privacy-preservation optimization since there is no requirement for the players to release their individual information directly, and the optimal strategies of other players can be deduced naturally. The difference between the user-independent hierarchical reverse game and the user-assisted one is that the game rules are stipulated entirely by the provider and the tenants (edge nodes) in the former, while some game rules can be customized according to the advice of users in the latter. This leads the user-independent hierarchical reverse game is more concise and easy to guarantee the incentive compatibility constraint, while the user-assisted hierarchical reverse game can attract more users since the game rules can adapt to their economic status, requirements, and preferences. © 1975-2011 IEEE.

关键词： Cloud platforms

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Global Graph Counterfactual Explanation: A Subgraph Mapping Approach

arXiv

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arXiv 2024年

作者： He, Yinhan Zheng, Wendy Zhu, Yaochen Ma, Jing Mishra, Saumitra Raman, Natraj Liu, Ninghao Li, Jundong Department of Electrical and Computer Engineering University of Virginia United States Department of Computer Science University of Virginia United States Department of Computer & Data Sciences Case Western Reserve University United States JP Morgan Chase & Co. United States School of Computing University of Georgia United States

Graph Neural Networks (GNNs) have been widely deployed in various real-world applications. However, most GNNs are black-box models that lack explanations. One strategy to explain GNNs is through counterfactual explanation, which aims to find minimum perturbations on input graphs that change the GNN predictions. Existing works on GNN counterfactual explanations primarily concentrate on the local-level perspective (i.e., generating counterfactuals for each individual graph), which suffers from information overload and lacks insights into the broader cross-graph relationships. To address such issues, we propose GlobalGCE, a novel global-level graph counterfactual explanation method. GlobalGCE aims to identify a collection of subgraph mapping rules as counterfactual explanations for the target GNN. According to these rules, substituting certain significant subgraphs with their counterfactual subgraphs will change the GNN prediction to the desired class for most graphs (i.e., maximum coverage). Methodologically, we design a significant subgraph generator and a counterfactual subgraph autoencoder in our GlobalGCE, where the subgraphs and the rules can be effectively generated. Extensive experiments demonstrate the superiority of our GlobalGCE compared to existing baselines. Our code can be found at https://***/r/GlobalGCE-92E8. © 2024, CC BY-NC-ND.

关键词： Graph neural networks

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Empowering Oral Healthcare: CNN-Assisted Diagnosis for Improved Patient Outcomes

Empowering Oral Healthcare: CNN-Assisted Diagnosis for Impro...

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data science and Information System (ICDSIS), IEEE International Conference on

作者： Vaishnavi Garrepalli Vishwanath Gupta S Abhishek T Anjali Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Amritapuri India Verizon Data Services India pvt Ltd Hyderabad India Rolls-Royce Inc Bangalore India

ISBN: (数字)9798350381665

ISBN: (纸本)9798350381672

In today’s world, health is very important, and among the many aspects of well-being, oral wellness is one of the crucial components. Diagnostic investigation in everyday clinical procedures primarily consists of reviewing periapical radiography, which visualizes dental anatomy such as teeth, bone, and soft tissues within the oral cavity. Nonetheless, subjective errors may arise during human interpretation of dental radiographs which also take a lot of time. Most of these dental X-rays have an important role in diagnosing various conditions like Implant, Fillings, Cavity, and Impacted teeth under accurate assessment of classification of dental radiographs. These imaging techniques can help dentists detect anomalies within the mouth through this diagnosis process. To improve the accuracy of diagnosis and reduce the costs of clinical operations, this study presents a novel approach that automates the detection of problematic teeth and other dental diseases using these x-ray images using CNN (Convolutional Neural Networks) architecture VGG19 for making the right diagnosis choices.

关键词： Accuracy Computational modeling Teeth X-rays Feature extraction Dentistry Convolutional neural networks

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DCVCP: Experimental Evaluation of Leukaemia Disease Prediction Using Deep Convoluted Vector Classification Principle

DCVCP: Experimental Evaluation of Leukaemia Disease Predicti...

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Innovative Computing, Intelligent Communication and Smart Electrical Systems (ICSES), International Conference on

作者： Bhuvaneswari Balachander S. Sivamurugan M Gomathi J. Jayanthi Adkane R.V M. Kanan Department of Electronics and Communication Engineering Dr.M.G.R Educational and Research Institute Chennai Department of Artificial Intelligence and Data Science Sri Sairam Engineering College Chennai Department of Cyber Security Dhanalakshmi College of Engineering Chennai Department of Computer Science and Engineering Sona College of Technology Salem Department of Mechanical Engineering Yeshwantrao Chavan College of Engineering Nagpur India Department of Industrial Engineering College of Engineering University of Business and Technology Jeddah Saudi Arabia Department of Mechanical Engineering College of Engineering Zarqa University Zarqa Jordan

ISBN: (数字)9798331543624

ISBN: (纸本)9798331543631

A wide variety of cancers, including skin, breast, lung, and blood malignancies such as lymphoma and leukemia, are defined by the uncontrolled growth of cells within the body. Acquired lymphoblastic leukemia is among the most prominent cancers. Blood cancer diagnoses are notoriously time-consuming because hematologists often make mistakes. With the use of cutting-edge tools like Deep Learning and Machine Learning, this study considers a fresh approach to leukemia classification. The precise diagnosis of blood cancer is a crucial medical process, but it is also one of the most costly and challenging for clinical staff to do. Blood cancer is one of many severe forms of the disease. The devastating and deadly blood cancers, lymphoma and leukemia, impact people of all ages and genders, and they are a leading cause of other illnesses and an elevated survival rate. Damage to and an increase in immature monocytes, neutrophils, eosinophils, and lymphocytes are hallmarks of both leukemia and lymphoma. Predicting and treating blood cancer early is crucial for improving survival rates in the health industry. These days, a number of manual methods exist for analyzing and predicting blood cancer based on microscopic medical reports of white blood cell images; this method is both reliable and deadly. It takes a lot of time and effort to manually forecast and analyze eosinophils, lymphocytes, monocytes, and neutrophils. The Deep Convoluted Vector Classification Principle (DCVCP) is a new deep learning technique for leukemia illness detection that was described in this study. To test how well it works, it was cross-validated with the current method, the Convolutional Neural Network (CNN). findings from this study show that DCVCP can effectively leverage data from network sensors to identify blood cancer, which might lead to faster findings and lower costs in clinical settings.

关键词： Deep learning Lymphocytes Leukemia Cells (biology) Vectors Convolutional neural networks Medical diagnostic imaging Eosinophils Tumors Systematic literature review

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A Survey on Cyber Attack Detection: Techniques, datasets, and Challenge

A Survey on Cyber Attack Detection: Techniques, Datasets, an...

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Research Methodologies in Knowledge Management, Artificial Intelligence and Telecommunication engineering (RMKMATE), International Conference on

作者： G Geetha Manjula Rajagopal K Purna Chand Department of Computing Technologies School of Computing (Research Scholor) SRM Institute of Science and Technology Kattankulathur Tamilnadu Department of Computing Technologies School of Computing (Assistant Professor) SRM Institute of Science and Technology Kattankulathur Tamilnadu Department of Computer Science and Engineering(Data Science) (Professor) B V Raju Institute of Technology Narsapur India

Networking technology is expanded along with cyber threats. Cyber threats are the instances which adversely influences the functional operations of the system or network. It results in various issues such as illegal access, data leakage, data stolen, DDoS attacks, etc. To overcome such issues, several intrusion detection systems are implemented over the past years. Though, these intrusion detection systems exhibit several limitations while trying to achieve excellent efficiency in attack detection. In this survey, totally 50 cyber-attack detection based research works are analysed under several perspectives. The analysis is done on several IDS techniques implemented in existing works. The reviewed learning-based approaches are explained in two major categories as ML and DL approaches. Here, the ML algorithms are further classified into three types: supervised, unsupervised and reinforcement learning approaches. Moreover, the existing works are also analysed in accordance with optimization algorithms and it is classified as metaheuristic and stochastic algorithms. In which, population-based algorithms and swarm-based algorithms are the categories classified under metaheuristic algorithms. In addition, the analyses are also performed on datasets usage and cyber-attack mitigation models. Furthermore, the analysis on existing works is done on performance analysis. And it is analysed under performance of cyber-attack detection models with specific description about maximum and minimum performance attained by recent works on cyber-attack detection models. Finally, the chronological review and challenges involved in existing cyber-attack detection models are discussed.

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The Estimation Analysis and Impact of Mobile Device Security on Wireless Network Security

The Estimation Analysis and Impact of Mobile Device Security...

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Smart Generation Computing, Communication and Networking (SMART GENCON), International Conference on

作者： Joyce Jacob J Varsha D. Jadhav Bhawna Singh Sangeetha Prasad Ranjan Ganguli D. Anandhasilambarasan Department of Electronics and Communication Engineering Prince Shri Venkateshwara Padmavathy Engineering College Chennai Department of Artificial Intelligence & Data Science Vishwakarma Institute of Information Technology Pune INDIA Department of Computer Science Mangalayatan University Aligarh India Department of Bussiness & Management Himalayan University Itanagar India Department Of Coputing & Information Technology Usha Martin University Ranchi India Department of Computer Science Engineering Karpagam Academy of Higher Education Coimbatore

Mobile tool security is essential in retaining wireless networks' security and integrity. The upward push in the deployment of cell tool technology has extensively elevated the assault floor of networks and exposed them to diffusion of protection threats. This paper offers an approach to the degree of the impact of cellular tool security on the general safety of wireless networks. The technique uses a mixture of static and dynamic analysis techniques to investigate the safety traits of cell devices and their effect on the security of Wi-Fi networks. The technique also defines metrics to assess the effectiveness of cell tool safety on the overall safety of the network. The evaluation outcomes show the significance of cellular tool protection for the general safety of the community and the effectiveness of the approach.

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Reconfigurable Holographic Surface: A New Paradigm for Ultra-Massive MIMO

arXiv

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arXiv 2024年

作者： Di, Boya Zhang, Hongliang Zhang, Rui Han, Zhu Song, Lingyang School of Electronics Peking University China School of Electronic and Computer Engineering Peking University Shenzhen Graduate School China School of Science and Engineering Shenzhen Research Institute of Big Data The Chinese University of Hong Kong Guangdong Shenzhen China Department of Electrical and Computer Engineering National University of Singapore Singapore Electrical and Computer Engineering Department University of Houston United States

Evolving from massive multiple-input multiple-output (MIMO) in current 5G communications, ultra-massive MIMO emerges as a seminal technology for fulfilling more stringent requirements of future 6G communications. However, widely-utilized phased arrays relying on active components make the implementation of ultra-massive MIMO in practice increasingly prohibitive from both cost and power consumption perspectives. In contrast, the development of reconfigurable holographic surface (RHS) provides a new paradigm to solve the above issue without the need of costly hardware components. By leveraging the holographic principle, the RHS serves as an ultra-thin and lightweight surface antenna integrated with the transceiver, which is a promising alternative to phased arrays for realizing ultra-massive MIMO. In this paper, we provide a comprehensive overview of the RHS, especially the RHS-aided communication and sensing. We first describe the basic concepts of RHS, and introduce its working principle and unique practical constraints. Moreover, we show how to utilize the RHS to achieve cost-efficient and high-performance wireless communication and sensing, and introduce the key technologies. In particular, we present the implementation of RHS with a wireless communication prototype, and report the experimental measurement results based on it. Finally, we outline some open challenges and potential future directions in this area. Copyright © 2024, The Authors. All rights reserved.

关键词： Antenna phased arrays

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Directed Undersampling Using Active Learning for Particle Identification 4th

Directed Undersampling Using Active Learning for Particle I...

引用

4th International Conference on Recent Innovations in Computing, ICRIC 2021

作者： Farou, Zakarya Ouaari, Sofiane Domian, Balint Horváth, Tomáš Department of Data Science and Engineering ELTE Eötvös Loránd University Pázmány Péter Sétány 1/C Budapest1117 Hungary Institute of Computer Science Pavol Jozef Šafárik University Jesenná 5 Košice040 01 Slovakia

ISBN: (纸本)9789811688911

Accurate particle identification is an ongoing task in the European organization for nuclear research, known as CERN where the challenge remains that targeted particles/events represent tiny minorities in front of the overwhelming presence of common particles such as protons. This paper presents a directed undersampling using an active learning method named DUAL to handle the high imbalance problem present in the particle identification dataset. The proposed approach was used to reduce the training set size while maintaining classifiers’ performance. Compared against various imbalance learning approaches, the experimental results show that using DUAL as a data reduction technique with a random forest classifier enhances classification performance in terms of Macro- F1 score and decreases the training time needed to train the models, which is very relevant while dealing with large-scale datasets. Despite being experimented only with particle identification dataset, we believe that DUAL could be adopted as a generic method for multi-class imbalanced classification problems with big data scale difficulties. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Decision trees

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Hierarchical Crowdsourcing for data Labeling with Heterogeneous Crowd

Hierarchical Crowdsourcing for Data Labeling with Heterogene...

引用

International Conference on data engineering

作者： Haodi Zhang Wenxi Huang Zhenhan Su Junyang Chen Di Jiang Lixin Fan Chen Zhang Defu Lian Kaishun Wu College of Computer Science and Software Engineering Shenzhen University Department of Computing Hong Kong Polytechnic University WeBank Institute of Financial Technology Shenzhen University Webank Co. Ltd. School of Data Science University of Science and Technology of China

With the rapid and continuous development of data-driven technologies such as supervised learning, high-quality labeled data sets are commonly required by many applications. Due to the easiness of crowdsourcing small tasks with low cost, a straightforward solution for label quality improvement is to collect multiple labels from a crowd, and then aggregate the answers. The aggregation strategies include majority voting and its many variants, EM-based approaches, Graph Neural Nets and so on. However, due to the uncertainty information loss and commonly existing task correlations, the aggregated labels usually contain errors and may damnify the downstream model *** address the above problem, we propose a hierarchical crowdsourcing framework 1 for data labeling with noisy answers about correlated data. We make use of the heterogeneity of the labeling crowd and form an initialization-checking-update loop to improve the quality of labeled data. We formalize and successfully solve the core optimization problem, namely, selecting a proper set of checking tasks for each round. We prove that maximizing the expected quality improvement is equivalent to minimizing the conditional entropy of the observations given the crowdsourced answer families for the selected task set, which is NP-hard to solve. Therefore, we design an efficient approximation algorithm and conduct a series of experiments on real data. The experimental results show that the proposed method effectively improves the quality of the labeled data sets as well as the SOTA performance, yet without extra human labor costs.

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Detection of Disease Through Clinical data Using Light Intensity Based Firefly Algorithm

Detection of Disease Through Clinical Data Using Light Inten...

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International Conference on Futuristic Technologies (INCOFT)

作者： Bibhuprasad Sahu Swarupa Pattanaik Jvr Ravindra Sasmita Pani Department of Artificial Intelligence and Data Science Vardhaman College of Engineering(Autonomous) Hyderabad India Department of Computer Science and Engineering Gandhi Institute for Education & Technology Bhubaneswar Odisha Department of Electronics & Communication Engineering Vardhaman College of Engineering(Autonomous) Hyderabad India Department of Computer Science and Engineering Balasore College of Engineering and Technology Balasore Odisha

In this study, we presented a hybrid gene selection model based on a novel multi-filter(MF) ensemble technique and light intensity difference-based Firefly Algorithm. A multifilter (ReliefF, Fisher Score, SU (Symmetrical Uncertainty), CFS (Correlation-based Feature Selection)) is applied to the original micro-array dataset to evaluate the best-ranked feature subsets individually. Ensemble multi-filer coupled with light intensity difference based firefly algorithm to achieve the optimized feature subset and at the last performance, evaluation is done using Support vector machine (SVM) by considering three different kernel parameters. From the experimental analysis, it is noteworthy to assure that the proposed methodology performs better than the existing approach. In this paper ensemble, a multi-filter method is used by implementing the concept of correlation-based redundancy and a Modified firefly algorithm (FA) called Light Intensity Difference Based Firefly Algorithm(LFA) is used as a wrapper to identify the optimal high-ranked feature subsets. So the selected subsets will be more robust for classification purposes. The correlation-based redundancy approach is used for the multi-filter case, where the top n genes of individuals are identified and a new dataset is prepared, which will be considered as input for the wrapper method. The main drawback behind considering this single filter may be trapped into the local optimum and may increase the complexity of the wrapper which may reduce the performance of the proposed model. Using multifilter (MF) not only considers all possible combinations of feature subsets by considering more than one filer but also provides a better opportunity to achieve better classification accuracy.

关键词： Support vector machines Performance evaluation Machine learning algorithms Uncertainty Redundancy Metaheuristics Machine learning

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