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International Joint Conference on Neural Networks (IJCNN)

作者： Peilin Wu Zhenhua Yang Peng Yang Department of Computer Science and Engineering Guangdong Provincial Key Laboratory of Brain-Inspired Intelligent Computation Southern University of Science and Technology Shenzhen China Department of Statistics and Data Science Research Institute of Trustworthy Autonomous Systems Southern University of Science and Technology Shenzhen China

ISBN: (数字)9798350359312

ISBN: (纸本)9798350359329

Zero-shot coordination studies the training of well-generalizing human-AI coordination agents in the scenario where human data is unavailable. To obtain a coordination agent generalize to unseen humans, prevailing methods generate a population of partner agents as proxy models of human partners and then train a coordination agent with these partner agents. Constructed partner agents are expected to be as diverse as possible to cover a wide range of human behaviors, preventing a distribution shift between training and testing stages. Recent works concentrate on studying effective methods of creating a group of high-reward while diverse partner agents to model unseen human partners. However, the resulting high-reward partner agents do not accurately reflect real-world situations, considering that human decisions are not always optimal and may sometimes even hinder the progression of coordination. Therefore, these studies still struggle to capture the potential characteristics of human partners. In this work, reinforcement learning (RL) and supervised learning (SL) are integrated to train a reward-conditioned policy. By conditioned on different desired rewards, a reward-conditioned policy simulates both low-reward and high-reward partners. Additionally, a reward-bucketed replay buffer and curriculum learning are applied to enhance reward diversity and boost the training of coordination agents. Experiments demonstrate that the proposed reward-conditioned policy is capable of generating agents with different rewards. Moreover, the zero-shot coordination performance of agents trained with these partners surpasses previous methods in the majority of scenarios within the Overcooked human-AI coordination benchmark.

关键词： Training Impedance matching Supervised learning Neural networks Reinforcement learning Benchmark testing Trajectory

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Comprehensive Approaches to Agricultural Intelligence: U-Net, YOLO, and Random Forest in Practice

Comprehensive Approaches to Agricultural Intelligence: U-Net...

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Communication, Control and Intelligent Systems (CCIS)

作者： Saharsh Gavas Pranali Kosamkar M. Tech Computer Science and Engineering (Data Science And Analytics) Dr. Vishwanath Karad MIT World Peace University Pune India Dept Computer Engineering and Technology Dr. Vishwanath Karad MIT World Peace University Pune India

ISBN: (数字)9798331528201

ISBN: (纸本)9798331528218

This paper seeks to implement and test advanced precision agricultural monitoring and detection systems derived from machine learning, remote sensing, and sensor technologies to improve pest detection, crop yields assessment, and animal husbandry management practices. The approach involves an automated and hierarchical framework integrating cutting edge machine learning technologies with fine spatial resolution remote sensing images and sensors. For instance, the authors apply CNN (convolutional neural networks) for pest identification; the deep learning models trained on Sentinel-2 images serve as yield predictors; UWB location coordinates and accelerometric measures combined form livestock behavior monitoring. This methodology encompasses the stages of implementation, testing and finally verification of the model. A variety of machine learning systems such as Faster-PestNet or deep semantic segmentation algorithms process data while remote sensing and the sensors’ data were used in predictions and classifications. This allows using different performance measures such as accuracy, precision recall rates and RMSE (root mean square error) to test the systems.

关键词： Precision agriculture YOLO Accuracy Estimation Production Livestock Convolutional neural networks Monitoring Remote sensing Crop yield

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Optimizing Wireless Resource Management and Synchronization in Digital Twin Networks

arXiv

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arXiv 2025年

作者： Yu, Hanzhi Liu, Yuchen Yang, Zhaohui Sun, Haijian Chen, Mingzhe Department of Electrical and Computer Engineering Frost Institute for Data Science and Computing University of Miami Coral GablesFL33146 United States Department of Computer Science North Carolina State University RaleighNC27695 United States College of Information Science and Electronic Engineering Zhejiang University Hangzhou310027 China School of Electrical and Computer Engineering University of Georgia AthensGA30602 United States

In this paper, we investigate an accurate synchronization between a physical network and its digital network twin (DNT), which serves as a virtual representation of the physical network. The considered network includes a set of base stations (BSs) that must allocate its limited spectrum resources to serve a set of users while also transmitting its partially observed physical network information to a cloud server to generate the DNT. Since the DNT can predict the physical network status based on its historical status, the BSs may not need to send their physical network information at each time slot, allowing them to conserve spectrum resources to serve the users. However, if the DNT does not receive the physical network information of the BSs over a large time period, the DNT’s accuracy in representing the physical network may degrade. To this end, each BS must decide when to send the physical network information to the cloud server to update the DNT, while also determining the spectrum resource allocation policy for both DNT synchronization and serving the users. We formulate this resource allocation task as an optimization problem, aiming to maximize the total data rate of all users while minimizing the asynchronization between the physical network and the DNT. The formulated problem is challenging to solve by traditional optimization methods, as each BS can only observe a partial physical network, making it difficult to find an optimal spectrum allocation strategy for the entire network. To address this problem, we propose a method based on the gated recurrent units (GRUs) and the value decomposition network (VDN). The GRU component allows the DNT to predict future status using the historical data, effectively updating itself when the BSs do not transmit the physical network information. The VDN algorithm enables each BS to learn the relationship between its local observation and the team reward of all BSs, allowing it to collaborate with others in determining whe

关键词： Resource allocation

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Enabling Collaborative and Green Generative AI Inference in Edge Networks

Enabling Collaborative and Green Generative AI Inference in ...

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IEEE Conference on Global Communications (GLOBECOM)

作者： Meng Tian Zhicheng Liu Chao Qiu Xiaofei Wang Dusit Niyato Victor C. M. Leung College of Intelligence and Computing Tianjin University Tianjin China College of Computing and Data Science Nanyang Technological University Singapore College of Computer Science and Software Engineering Shenzhen University Shenzhen China Department of Electrical and Computer Engineering The University of British Columbia Vancouver Canada

ISBN: (数字)9798350351255

ISBN: (纸本)9798350351262

Recent advances in the diffusion model mark a significant leap in AI-generated image technology while extending its application to the Internet of Things (IoT). However, deploying these models on resource-constrained edge devices presents considerable challenges, primarily due to their high computational energy demands and stringent quality requirements. In response to these challenges, we introduce a collaborative inference system tailored for green edge networks. First, we propose a multi-exit U-ViT model that achieves the balance between inference quality and processing speed by allowing adaptive selection of exit points during diffusion for efficient processing. Next, we develop a novel generative AI-assisted reinforcement learning algorithm that controls the exit selections and offloading decisions of the device to achieve maximum global gain. Furthermore, we design a novel policy network incorporating an attention-based state-embedded policy network to enhance the algorithm’s ability to perceive and make decisions about the state of the environment. Experimental results demonstrate that our system achieves energy-efficient inference while ensuring the quality of the generated content.

关键词： Energy consumption Adaptation models Generative AI Computational modeling Image edge detection Green products Collaboration Inference algorithms Energy efficiency Internet of Things

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arXiv

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arXiv 2023年

作者： Rácz, Miklós Z. Sridhar, Anirudh Department of Statistics and Data Science Department of Computer Science Northwestern University EvanstonIL United States Department of Electrical and Computer Engineering Princeton University PrincetonNJ United States

We consider the task of estimating the latent vertex correspondence between two edge-correlated random graphs with generic, inhomogeneous structure. We study the so-called k-core estimator, which outputs a vertex correspondence that induces a large, common subgraph of both graphs which has minimum degree at least k. We derive sufficient conditions under which the k-core estimator exactly or partially recovers the latent vertex correspondence. Finally, we specialize our general framework to derive new results on exact and partial recovery in correlated stochastic block models, correlated Chung-Lu graphs, and correlated random geometric graphs. Copyright © 2023, The Authors. All rights reserved.

关键词： Graphic methods

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Intelligent Blocking and Prevention of SARS-CoV-2 Based on Evolutionary Reinforcement Learning

Intelligent Blocking and Prevention of SARS-CoV-2 Based on E...

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Congress on Evolutionary Computation

作者： Chengwei He Huaping Hong Lijia Ma Qingling Zhu Yuan Bai College of Computer Science and Software Engineering Shenzhen University Shenzhen China National Engineering Laboratory for Big Data System Computing Technology Shenzhen University Shenzhen China

ISBN: (数字)9798350308365

ISBN: (纸本)9798350308372

In recent years, the global repercussions of SARS-CoV-2 and its variants have posed significant challenges to various areas, including the economic order, transportation, healthcare, and education, and the mitigation and prevention of SARS-CoV-2 and other infectious diseases have received much attention. This paper tries to develop a rational and effective intervention strategy in response to the dynamic evolution of the SARS-CoV-2 epidemic. To address this issue, we first use the SEIR model to analyze the population distribution under the SARS-CoV-2 epidemic, and then integrate a deep Q-neural network (DQN) to reflect an economic quantification model contextualized within the SARS-CoV-2 scenario. Next, we propose an evolutionary deep reinforcement learning to generate an optimal and adaptive intervention strategy, by optimizing the weight sequence of the DQN. The experimental results show the effectiveness of the proposed method in maximizing economic benefits, while ensuring the uninterrupted functionality of medical institutions. Importantly, the learned intervention strategy exhibits the applicability to other infectious diseases.

关键词： Economics Epidemics Infectious diseases Prevention and mitigation Heuristic algorithms Sociology Loss measurement

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A Unified Framework to Assess Market Implications of Institutional Investments

A Unified Framework to Assess Market Implications of Institu...

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2022 IEEE International Conference on Big data, Big data 2022

作者： Seth, Taruna Tiu, Cristian Chaudhary, Vipin University at Buffalo Department of Computer Science and Engineering Buffalo United States University at Buffalo Department of Finance Buffalo United States Case Western Reserve University Department of Computer and Data Sciences Cleveland United States

ISBN: (纸本)9781665480451

US financial markets are influenced by complex interactions of diverse entities like institutional investors, which control a considerable portion of all US financial assets. Despite significant increase in the institutional ownership over the last several years, detection of causal associations between institutional investments and equity markets remains elusive due to inherent intricacies of the investment behavior. In this paper, we propose a novel solution to establish linkages between the institutional investments and market dynamics. We accomplish this task by deploying a multi-stage methodology that includes evaluation of heterogeneous data from disparate sources, an integrated framework comprising of tools to facilitate unstructured- and structured- data modeling, data integration, unsupervised learning, and an evaluation approach to uncover discernible patterns capturing market fragility. Our results on the real data confirm the efficacy of the proposed solution by establishing linkages between the investment behavior and market movements. For instance, the results show that the co-ownership and selling of large capitalization stocks held by institutional investors drive market returns co-movements. Our results confirm the efficacy of the presented framework. The proposed solution can assist the economists and policy makers detect fraud and proactively prepare against disruptive market movements thereby minimizing the risk to the economy. © 2022 IEEE.

关键词： Investments

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Optimal Multi-Keyword Ranked Search Matching in Public Cloud with Enhanced Secure Search Scheme Over Encrypted data

Optimal Multi-Keyword Ranked Search Matching in Public Cloud...

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International Conference on science Technology engineering and Management (ICONSTEM)

作者： B. Radha Jaya Bharathi R R. Vidhya Muthulakshmi K. Vidhya R. Vijaya Kumar Reddy Ruchika Rondon Department of ICT and Cognitive Systems Sri Krishna Arts and Science College Coimbatore Tamil Nadu India Department of Computer Science Engineering RMK College of Engineering and Technology Puduvoyal Tamil Nadu India Department of Artificial Intelligence and Data Science Panimalar Engineering College Chennai Tamil Nadu India Department of Computer Science Engineering KPR Institute of Engineering and technology Arasur Tamil Nadu India Department of Computer Science Engineering Lakshmaiah Education Foundation Vaddeswaram Andhra Pradesh Department of Petroleum Engineering Graphic Era Deemed to be University Dehradun Uttarakhand India

ISBN: (数字)9798350365092

ISBN: (纸本)9798350365108

In this research work, the authors present an improved secure search algorithm that will allow for optimum multi-keyword ranked search matching in public cloud storage that uses encrypted data. The goal of the plan is to provide a method that is both safe and effective for searching for and retrieving important data stored in cloud settings, all while preserving the data's privacy and confidentiality. Experiments and simulations are carried out in order to assess the viability of the suggested plan, and mathematical expressions are used to depict the algorithms and assessment criteria. According to the findings of the research, the strategy that was presented is capable of achieving a high level of accuracy, recall, and F1-score while simultaneously reducing the number of false positives and false negatives. The research underlines how important it is to address security problems in cloud computing settings, and it gives significant insights that can be used to design cloud computing services that are both safe and efficient. In the future, possible topics of study might include further refining and optimizing of the scheme that was described, as well as the application of the method in real cloud computing systems.

关键词： Cloud computing data privacy Accuracy Refining Mathematical models Cryptography STEM

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PrintListener: Uncovering the Vulnerability of Fingerprint Authentication via the Finger Friction Sound

arXiv

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arXiv 2024年

作者： Zhou, Man Su, Shuao Wang, Qian Li, Qi Zhou, Yuting Ma, Xiaojing Li, Zhengxiong School of Cyber Science and Engineering Huazhong University of Science and Technology China School of Cyber Science and Engineering Wuhan University China Institute for Network Sciences and Cyberspace Tsinghua University China Department of Computer Science and Engineering University of Colorado Denver United States Hubei Key Laboratory of Distributed System Security Hubei Engineering Research Center on Big Data Security School of Cyber Science and Engineering Huazhong University of Science and Technology China

Fingerprint authentication has been extensively employed in contemporary identity verification systems owing to its rapidity and cost-effectiveness. Due to its widespread use, fingerprint leakage may cause sensitive information theft, enormous economic and personnel losses, and even a potential compromise of national security. As a fingerprint that can coincidentally match a specific proportion of the overall fingerprint population, MasterPrint rings the alarm bells for the security of fingerprint authentication. In this paper, we propose a new side-channel attack on the minutiae-based Automatic Fingerprint Identification System (AFIS), called PrintListener, which leverages users’ fingertip swiping actions on the screen to extract fingerprint pattern features (the first-level features) and synthesizes a stronger targeted PatternMasterPrint with potential second-level features. The attack scenario of PrintListener is extensive and covert. It only needs to record users’ fingertip friction sound and can be launched by leveraging a large number of social media platforms. Extensive experimental results in real-world scenarios show that Printlistener can significantly improve the attack potency of MasterPrint. © 2024, CC0.

关键词： Friction

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Comparative Study of Customized CNN Model and Transfer Learning for Brain Tumor Classification

Comparative Study of Customized CNN Model and Transfer Learn...

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Network, Multimedia and Information Technology (NMITCON) International Conference on

作者： Vaishnavi N. Sinchana K. Ashwini Kodipalli Trupthi Rao Kumarswamy S Department of Computer Science and Engineering Global Academy of Technology Banglore Karnataka India Department of Artificial Intelligence and Data Science and Engineering Global Academy of Technology Bangalore Karnataka India

Brain tumors are abnormal cell growths in the brain, which can be cancerous or non-cancerous. They have diverse effects, including cognitive and neurological impairments, as well as personality and behavioral changes. This study proposes a multiclass classification framework utilizing a plain CNN architecture and Transfer learning to accurately identify brain tumor types: meningioma, glioma, pituitary tumor, and no tumor. We have explored transfer learning models such as VGG16, AlexNet, and ResNet50, these are initialized with pre-trained weights and fine-tuned on a comprehensive brain tumor dataset which has an equal distribution among classes. Optimizers such as Adam, AdaDelta, and SGD are modified to enhance performance. AdaDelta was used as the VGG16 optimizer, and it helped us achieve the highest accuracy of 99.83% with a loss of 0.74%. ResNet50 on the other hand, had the lowest accuracy 70% and highest loss 66.68%. While AlexNet without dropouts and AdaDelta as optimizer gives highest loss of 92.83%. The custom CNN model's loss was 0.04%, which is the lowest of any of them. The findings contribute to robust tools for accurate brain tumor diagnosis and treatment planning.

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