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FedSC: Provable Federated Self-supervised Learning with Spectral Contrastive Objective over Non-i.i.d. data 41

FedSC: Provable Federated Self-supervised Learning with Spec...

引用

41st International Conference on Machine Learning, ICML 2024

作者： Jing, Shusen Yu, Anlan Zhang, Shuai Zhang, Songyang Department of Radiation Oncology University of California San FranciscoCA United States Department of Electrical and Computer Engineering Lehigh University BethlehemPA United States Department of Data Science New Jersey Institute of Technology NewarkNJ United States Department of Electrical and Computer Engineering University of Louisiana at Lafayette LafayetteLA United States

Recent efforts have been made to integrate self-supervised learning (SSL) with the framework of federated learning (FL). One unique challenge of federated self-supervised learning (FedSSL) is that the global objective of FedSSL usually does not equal the weighted sum of local SSL objectives. Consequently, conventional approaches, such as federated averaging (FedAvg), fail to precisely minimize the FedSSL global objective, often resulting in suboptimal performance, especially when data is non-i.i.d.. To fill this gap, we propose a provable FedSSL algorithm, named FedSC, based on the spectral contrastive objective. In FedSC, clients share correlation matrices of data representations in addition to model weights periodically, which enables inter-client contrast of data samples in addition to intra-client contrast and contraction, resulting in improved quality of data representations. Differential privacy (DP) protection is deployed to control the additional privacy leakage on local datasets when correlation matrices are shared. We also provide theoretical analysis on the convergence and extra privacy leakage. The experimental results validate the effectiveness of our proposed algorithm. Copyright 2024 by the author(s)

关键词： Federated learning

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A Novel Text Clustering Approach Based on Bacterial Colony Optimization 7

A Novel Text Clustering Approach Based on Bacterial Colony O...

引用

7th International Conference on Electronics, Communication and Aerospace Technology, ICECA 2023

作者： Dhanalakshmi, S. Sathiyabama, S. Ayyamuthukumar, D. Thiruvalluvar Government Arts College Department of Computer Science Tamilnadu Rasipuram India Muthayammal College of Engineering Department of Artificial Intelligence and Data Science Rasipuram India

ISBN: (纸本)9798350340600

A huge number of text documents are divided into a limited number of groups using the unsupervised learning technique known as text clustering. While the clusters contain different text documents, each cluster contains similar documents. Swarm intelligence (SI) optimization methods have been successfully used to resolve a variety of optimization issues, including difficulties with text document grouping. However, the traditional SI algorithms have many drawbacks including the local optima, low convergence rate, and low accuracy. Hence, the present research work proposes a novel text clustering approach based on bacterial colony optimization (BCO) for separating text documents based on similarity. Three separate text document datasets are used for the experiments, and performance is assessed using three different performance measures. The novel BCO approach produces high accuracy and a quick convergence rate, according to the analysis of the results. The suggested BCO text clustering strategy compares several text clustering methods to analyze strength and robustness. © 2023 IEEE.

关键词： Cluster analysis

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Sequential Image Storytelling Model Based on Transformer Attention Pooling 38

Sequential Image Storytelling Model Based on Transformer Att...

引用

38th International Conference on Image and Vision Computing New Zealand, IVCNZ 2023

作者： Malakan, Zainy M. Hassan, Ghulam Mubashar Mian, Ajmal The University of Western Australia Department of Computer Science and Software Engineering Perth Australia Umm Al-Qura University Department of Data Science Makkah Saudi Arabia

ISBN: (纸本)9798350370515

The Visual Storytelling Task (VST) extends beyond describing a single image, such as image captioning, to sequential image descriptions in the form of a coherent story. However, such descriptions present challenges in handling varying language styles, relational role-modeling, consistency, and events not evident in individual images. A common limitation in existing approaches is their inability to fully describe relations and visual changes, leading to stories devoid of linguistic cohesion between multiple sentences. To address this challenge, we introduce a novel framework, Sequential Image Storytelling Model (SISM), based on the Transformer architecture. Our model contextualizes input images by dividing them into multiple 16 × 16 patches and associates them with language content using the encoder-decoder technique. It incorporates cross-attention and attention pooling to identify the most relevant relationships between input images. Our proposed model achieves state-of-the-art performance on the recently published SSID dataset and performs competitively on VIST dataset, achieving the top score on BLEU-1 metric. © 2023 IEEE.

关键词： Modeling languages

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Leaf Disease Identification using ResNet

Leaf Disease Identification using ResNet

引用

2023 International Conference on Artificial Intelligence and Knowledge Discovery in Concurrent engineering, ICECONF 2023

作者： Sirenjeevi, P. Karthick, J. Madhu Agalya, K. Srikanth, R. Elangovan, T. Nareshkumar, R. Department of Artificial Intelligence and Data Science Srm Valliammai Engineering College Kattankulathur Chennai India Department of Computer Science and Engineering A.V.C. College of Engineering Mayiladuthurai India Department of Computer Science and Engineering Sri Eshwar College of Engineering Coimbatore India Department of Computer Science and Engineering Karpagam Academy of Higher Education Eachanari Coimbatore India Department of Networking and Communication SRM Institute of Science and Technology Kattankulathur Chennai India

ISBN: (纸本)9798350334357

The study of leaf diseases, as well as their detection and diagnosis, has been the subject of an increasing amount of research and attention as intelligent agricultural systems have become increasingly common and widely used. In order to explore the detection and classification of apple leaf illnesses, we made use of data sets containing examples of apple grey-spot disease, black star disease, cedar rust disease, and healthy leaves. SVM classifier for image segmentation, ResNet and VGG convolutional neural network models were utilized for comparison and improvement respectively. In our prosed method ResNet-18, which had less layers of the ResNet network, achieved greater recognition effects by obtaining an accuracy rate of 98.5 percent. © 2023 IEEE.

关键词： Convolutional neural networks

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Federated Natural Policy Gradient and Actor Critic Methods for Multi-task Reinforcement Learning 38

Federated Natural Policy Gradient and Actor Critic Methods f...

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38th Conference on Neural Information Processing Systems, NeurIPS 2024

作者： Yang, Tong Cen, Shicong Wei, Yuting Chen, Yuxin Chi, Yuejie CMU United States UPenn United States Department of Electrical and Computer Engineering Carnegie Mellon University United States Department of Statistics and Data Science Wharton School University of Pennsylvania United States

Federated reinforcement learning (RL) enables collaborative decision making of multiple distributed agents without sharing local data trajectories. In this work, we consider a multi-task setting, in which each agent has its own private reward function corresponding to different tasks, while sharing the same transition kernel of the environment. Focusing on infinite-horizon Markov decision processes, the goal is to learn a globally optimal policy that maximizes the sum of the discounted total rewards of all the agents in a decentralized manner, where each agent only communicates with its neighbors over some prescribed graph topology. We develop federated vanilla and entropy-regularized natural policy gradient (NPG) methods in the tabular setting under softmax parameterization, where gradient tracking is applied to estimate the global Q-function to mitigate the impact of imperfect information sharing. We establish non-asymptotic global convergence guarantees under exact policy evaluation, where the rates are nearly independent of the size of the state-action space and illuminate the impacts of network size and connectivity, and further establish its robustness against inexact policy evaluation. We further propose a federated natural actor critic (NAC) method for multi-task RL with function approximation and stochastic policy evaluation, and establish its finite-time sample complexity taking the errors of function approximation into account. To the best of our knowledge, this is the first time that near dimension-free global convergence is established for federated multi-task RL using policy optimization. © 2024 Neural information processing systems foundation. All rights reserved.

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Recommendations of Smartphone Applications According to Customer Reviews and Capabilities 2

Recommendations of Smartphone Applications According to Cust...

引用

2nd International Conference on Augmented Intelligence and Sustainable Systems, ICAISS 2023

作者： Ramnath, M. Rubavathi, C. Yesubai Ramco Institute of Technology Department of Artificial Intelligence and Data Science Tamil Nadu Rajapalayam India Francis Xavier Engineering College Department of Computer Science and Engineering Tamil Nadu Tirunelveli India

ISBN: (纸本)9798350325799

There are a plethora of new Android portable apps available now. As a result, making sure the apps you install on your mobile device are safe is a daunting undertaking for the average user. This study provides a popular and secure mobile App with a recommendation system to streamline the process. The design focus is on making recommendations for mobile apps by gauging their popularity and potential security issues. To index the apps and save the information in a database, a web crawler is employed. The programs are then sorted into groups according to their popularity and ratings. The suggested Android app provides a list of applications from the Google Play Store along with their respective security ratings whenever a query is executed. How popular an app is and what permissions it requires from the user significantly increase the app's potential security risks. This work aims to create such a recommendation system without sacrificing security or user engagement. © 2023 IEEE.

关键词： Recommender systems

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GeoAI in Resource-Constrained Environments

GeoAI in Resource-Constrained Environments

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2024 IEEE International Conferences of AI, Science, engineering, and Technology, AIxSET 2024

作者： Sughiarta, Gede Kurnianingsih, Atiek Gopaladinne, Srikar Reddy Shrivastava, Sujay Gorla, Hemanth Kumar Reddy Bohlen, Marc Wisnu foundation Denpasar Indonesia University at Buffalo Department of Computer Science and Engineering Buffalo United States Institute for Artificial Intelligence and Data Science University at Buffalo Department of Art Buffalo United States

ISBN: (纸本)9798350390995

This paper describes spatially aware Artificial Intelligence, GeoAI, tailored for small organizations such as NGOs in resource constrained contexts where access to large datasets, expensive compute infrastructure and AI expertise may be restricted. We furthermore consider future scenarios in which resource-intensive, large geospatial models may homogenize the representation of complex landscapes, and suggest strategies to prepare for this condition. © 2024 IEEE.

关键词： artificial intelligence and non-government organizations foundation models GeoAI local knowledge resource constraints

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Optimizing One-Stage Object Detection for URLLC: A Two-Stage Pipeline with Oculi SPU 25

Optimizing One-Stage Object Detection for URLLC: A Two-Stage...

引用

25th International Arab Conference on Information Technology, ACIT 2024

作者： Moghlebey, Adam Bou Moubayed, Abdallah Injadat, MohammadNoor Lebanese American University Electrical & Computer Engineering Department Byblos Lebanon King Fahd University of Petroleum & Minerals Computer Engineering Department Interdisciplinary Research Center for Intelligent Secure Systems Dhahran Saudi Arabia Zarqa University Data Science & Artificial Intelligence Department Zarqa Jordan University of Business and Technology Jeddah Saudi Arabia

ISBN: (纸本)9798331540012

Pedestrian-related accidents account for an estimated 20-25% of the approximately 1.19 million road fatalities occurring annually, highlighting the urgent need for enhanced detection systems in surveillance and smart transportation to mitigate such incidents. The escalating demand for Ultra-Reliable Low-Latency Communications (URLLC) in modern surveillance and transportation systems has become a solution to that problem but highlighted significant challenges in reducing inference times of machine learning models, particularly on constrained hardware. The maximum permissible latency for most vehicular applications ranges from 6 to 20 milliseconds based on the application. When tested on an 8GB RAM setup using the NTU, LLVIP, and VIRAT datasets, YOLOv8n achieved a minimum inference time of approximately 200 milliseconds per frame, making it unsuitable for URLLC applications despite 8GB RAM being an adequate hardware setup for edge devices. This research introduces a new two-stage pipeline that integrates Oculi's Sensing and Processing Unit (SPU) simulation with the YOLOv8n one-stage object detection model to enhance pedestrian detection, thereby addressing these challenges. By leveraging Regions of Interest (ROIs) generated by Oculi's SPU, our approach optimizes the input data for the YOLO model, substantially reducing computational overhead and thus making it suitable for URLLC applications. This integration can lead to a reduction in inference time by up to 93% in the tested frames when being compared to YOLOv8n pre-trained model. © 2024 IEEE.

关键词： Digital storage

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Diabetes Prediction Using Machine Learning Techniques: A Case Study from Jordan 25

Diabetes Prediction Using Machine Learning Techniques: A Cas...

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25th International Arab Conference on Information Technology, ACIT 2024

作者： Alazaidah, Raed Batyha, Radwan Samara, Ghassan Al Henawi, Esraa Al-Safarini, Maram Y. Samhan, Ahmad A. Elrashidi, Ali Alluwaici, Mo'Ath Al-Omari, Deana It Zarqa University Department of Data Science Ai Faculty Zarqa Jordan Private University Department of Computer Science Faculty of It Applied Science Amman Jordan Zarqa University Department of Computer Science Faculty of It Zarqa Jordan Zarqa University Department of Software Engineering Faculty of It Zarqa Jordan University of Business and Technology Department of Electrical Engineering Jeddah21432 Saudi Arabia

ISBN: (纸本)9798331540012

Diabetes is one of the most common diseases in Jordan. It is the main reason of death among Jordanian adult citizens. Worldwide, 48% of all deaths are due to Diabetes occurred before the age of 70 years. Hence, this research is interested in the early prediction of this disease among Jordanian people. To achieve this main objective, Machine Learning (ML) is utilized through a large number of classification models and considering five well-known evaluation metrics. These classification models have been trained on a primary dataset that has been collected for the purpose of the research. The results revealed that BayesNet and NaiveBayes are the best classification models to handle the task of the early prediction of Diabetes in Jordan © 2024 IEEE.

关键词： Adversarial machine learning

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Cloud-Based Oxygen Management System with Seamless HTTP Protocol Integration 4

Cloud-Based Oxygen Management System with Seamless HTTP Prot...

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4th International Conference on Innovative Practices in Technology and Management, ICIPTM 2024

作者： Hemalatha, S. Ramasamy, Jayabharathi Selvi, K. Anandan, P. Meenakshi, R. Maniraj, S.P. Tagore Engineering College Department of Artificial Intelligence and Data Science Tamil Nadu Chennai India Rmk College of Engineering and Technology Department of Computer Science and Engineering Tamil Nadu Chennai India S. A. Engineering College Faculty of Management Studies Tamil Nadu Chennai India Saveetha Institute of Medical and Technical Sciences Saveetha School of Engineering Department of Electronics and Communication Engineering Tamil Nadu Chennai India Chennai Institute of Technology Department of Computer Science and Engineering Tamil Nadu Chennai India Srm Institute of Science and Technology Department of Computer Science and Engineering Tamil Nadu Chennai India

ISBN: (纸本)9798350307757

In this study, an efficient system to improve the oxygen supply in hospitals and other healthcare institutions is proposed. It uses Internet of things (IoT) devices and cloud based data transmission and processing system. The controller unit collects the oxygen sensor data for the analysis and uses HTTP protocols to send and receive data with cloud computing in HTTP website server. The cloud is data storage, processes and real-time monitoring, analysis and control system. It has a user interface with viewed administrators and medical professionals. The interface provides real-time data regarding oxygen supply, consumption patterns, and possible concerns. Authorized workers can access and manage the system via the display, allowing for timely adjustments to oxygen supply settings, optimizations to refill schedules, and responses to emergency circumstances. It's adaptable, flexible, and resilient, which are benefits. There will be less need to worry about oxygen shortages and interruptions, and healthcare institutions of all sizes can benefit from this. The cloud's remote access and control capabilities provide continuous functioning and rapid reaction times. It's an effective solution for hospitals and other healthcare facilities that need to control their oxygen supplies. With its cloud-based design, the solution increases productivity, improves patient safety, and simplifies oxygen management. © 2024 IEEE.

关键词： HTTP

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