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21st International Conference on Autonomous Agents and Multiagent Systems, AAMAS 2022

作者： Ran, Dezhi Zheng, Weiqiang Li, Yunqi Bian, Kaigui Zhang, Jie Deng, Xiaotie School of Computer Science Peking University China Department of Computer Science Yale University United States School of Computer Science National Engineering Laboratory for Big Data Analysis and Applications Peking University China Electronics and Computer Science University of Southampton United Kingdom Center on Frontiers of Computing Studies Peking University China

ISBN: (纸本)9781713854333

A new mobile attention economy has emerged with the explosive growth of short-video apps such as TikTok. In this internet market, three types of agents interact with each other: the platform, influencers, and advertisers. A short-video platform encourages its influencers to attract users by creating appealing content through short-form videos and allows advertisers to display their ads in short-form videos. There are two options for the advertisers: one is to bid for platform advert slots in a similar way to search engine auctions;the other is to pay an influencer to make engaging short videos and promote them through the influencer's channel. The second option will generate a higher conversion ratio if advertisers choose the right influencers whose followers match their target market. Although displaying influencer ads will generate less revenue, it is more engaging than platform ads, which is better for maintaining user traffic. Therefore, it is crucial for a platform to balance these factors by establishing a sustainable business agreement with its influencers and advertisers. In this paper, we develop a two-stage solution for a platform to maximize short-term revenue and long-term user traffic maintenance. In the first stage, we estimate the impact of user traffic generated by displaying influencer ads and characterize the user traffic the platform should allocate to influencers for overall revenue maximization. In the second stage, we devise an optimal (1 - 1/e)-competitive algorithm for ad slot allocation. To complement this analysis, we examine the ratio of the revenue generated by our online algorithm to the optimal offline revenue. Our simulation results show that this ratio is 0.94 on average, which is much higher than (1 - 1/e) and outperforms four baseline algorithms. © 2022 International Foundation for Autonomous Agents and Multiagent Systems (***). All rights reserved

关键词： Multi agent systems

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SegMorph: Concurrent Motion Estimation and Segmentation for Cardiac MRI Sequences

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IEEE Transactions on Medical Imaging 2024年 PP卷 1-1页

作者： Bi, Ning Zakeri, Arezoo Xia, Yan Cheng, Nina Taylor, Zeike A. Frangi, Alejandro F. Gooya, Ali University of Leeds Leeds UK Christabel Pankhurst Institute Division of Informatics Imaging and Data Sciences School of Health Sciences Faculty of Biology Medicine and Health University of Manchester Manchester UK CISTIB the School of Computing University of Leeds Leeds UK CISTIB School of Mechanical Engineering University of Leeds UK Christabel Pankhurst Institute Department of Computer Science Faculty of Science and Engineering and the Division of Informatics Imaging and Data Science Faculty of Biology Medicine and Health The University of Manchester Manchester UK School of Computing Science University of Glasgow Glasgow UK

We propose a novel recurrent variational network, SegMorph, to perform concurrent segmentation and motion estimation on cardiac cine magnetic resonance image (CMR) sequences. Our model establishes a recurrent latent space that captures spatiotemporal features from cine-MRI sequences for multitask inference and synthesis. The proposed model follows a recurrent variational auto-encoder framework and adopts a learnt prior from the temporal inputs. We utilise a multi-branch decoder to handle bi-ventricular segmentation and motion estimation simultaneously. In addition to the spatiotemporal features from the latent space, motion estimation enriches the supervision of sequential segmentation tasks by providing pseudo-ground truth. On the other hand, the segmentation branch helps with motion estimation by predicting deformation vector fields (DVFs) based on anatomical information. Experimental results demonstrate that the proposed method performs better than state-of-the-art approaches qualitatively and quantitatively for both segmentation and motion estimation tasks. We achieved an 81% average Dice Similarity Coefficient (DSC) and a less than 3.5 mm average Hausdorff distance on segmentation. Meanwhile, we achieved a motion estimation Dice Similarity Coefficient of over 79%, with approximately 0.14% of pixels displaying a negative Jacobian determinant in the estimated DVFs. IEEE

关键词： Motion estimation

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Unprecedented Smart Algorithm for Uninterrupted SDN Services During DDoS Attack

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Computers, Materials & Continua 2022年第1期70卷 875-894页

作者： Muhammad Reazul Haque Saw Chin Tan Zulfadzli Yusoff Kashif Nisar Rizaludin Kaspin Iram Haider Sana Nisar J.P.C.Rodrigues Bhawani Shankar Chowdhry Muhammad AslamUqaili Satya Prasad Majumder Danda B.Rawat Richard Etengu Rajkumar Buyya Faculty of Computing&Informatics Multimedia UniversityPersiaranMultimediaCyberjaya63100SelangorMalaysia Faculty of Engineering Multimedia UniversityPersiaran MultimediaCyberjaya63100SelangorMalaysia Faculty of Computing and Informatics University Malaysia SabahJalan UMSKota Kinabalu Sabah88400Malaysia Telekom Malaysia Research&Development TM Innovation Centre63000CyberjayaSelangorMalaysia Federal University of Piauí(UFPI) TeresinaPIBrazil Instituto de Telecomunica珲es 6201-001Covilh?Portugal National Center of Robotics and Automation-Condition Monitoring Systems Lab MUETJamshoroPakistan Department of Electrical and Electronic Engineering BUETDhaka1205Bangladesh Department of Electrical Engineering and Computer Science Data Science and Cybersecurity CenterHoward UniversityWashingtonDCUSA Cloud Computing and Distributed Systems(CLOUDS)Laboratory School of Computing and Information SystemsThe University of MelbourneMelbourneVIC 3053Australia

In the design and planning of next-generation Internet of Things(IoT),telecommunication,and satellite communication systems,controller placement is crucial in software-defined networking(SDN).The programmability of the SDN controller is sophisticated for the centralized control system of the entire ***,it creates a significant loophole for the manifestation of a distributed denial of service(DDoS)attack ***,recently a Distributed Reflected Denial of Service(DRDoS)attack,an unusual DDoS attack,has been ***,minimal deliberation has given to this forthcoming single point of SDN infrastructure failure ***,recently the high frequencies of DDoS attacks have increased *** this paper,a smart algorithm for planning SDN smart backup controllers under DDoS attack scenarios has *** proposed smart algorithm can recommend single or multiple smart backup controllers in the event of DDoS *** obtained simulated results demonstrate that the validation of the proposed algorithm and the performance analysis achieved 99.99%accuracy in placing the smart backup controller under DDoS attacks within 0.125 to 46508.7 s in SDN.

关键词： SDN smart algorithm RTZLK-DAASCP DDoS attack DRDoS

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Editorial: Synopsis of Core Concepts for Explainable-AI-Ready data and Models

Editorial: Synopsis of Core Concepts for Explainable-AI-Re...

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International Workshop on Designing the Conceptual Landscape for a XAIR Validation Infrastructure, DCLXVI 2024

作者： Horsch, Martin Thomas Scholze, Sebastian Al Machot, Fadi Department of Data Science Faculty of Science and Technology Norwegian University of Life Sciences P.O. Box 5003 Ås1432 Norway Scientific Computing Department UKRI Science and Technology Facilities Council Daresbury Laboratory Keckwick Ln WarringtonWA4 4AD United Kingdom ATB – Institut für angewandte Systemtechnik Bremen GmbH Wiener Str. 1 Bremen28359 Germany

ISBN: (纸本)9783031892738

Reliable AI technology necessitates the development of robust validation infrastructures and data documentation schemas to ensure that AI systems become trustworthy and transparent. The International Workshop on Designing the Conceptual Landscape for a XAIR Validation Infrastructure (DCLXVI 2024), held on 11th December 2024 in Kaiserslautern, Germany, surveyed the conceptual landscape of explainable-AI-ready (XAIR) models and data. The definition of concepts relevant to data integrity, algorithmic transparency, and user interpretability were explored, resulting in a synopsis of core concepts for "XAIR." © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Applied ontology explainable-AI-readiness FAIR data

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An Empirical Design of IoT based Health Surveillance Scheme for Coronavirus Affected Patients 8

An Empirical Design of IoT based Health Surveillance Scheme ...

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8th IEEE International Conference on science, Technology, Engineering and Mathematics, ICONSTEM 2023

作者： Swarna, M. Geetha, R. Saranya, G. Shakthi Murugan, K.H. Gnanaprakasam, C. Dr. M.G.R. Educational and Research Institute University Department of Electronics and Communication Engineering Tamilnadu Chennai600095 India Srm Institute of Science and Technology Department of Computing Technologies Chennai India Sri Krishna College of Engineering and Technology Department of Electronics and Communication Engineering Coimbatore641008 India Narayana Engineering College Department of Electronics and Communication Engineering AndhraPradesh Gudur524101 India Panimalar Engineering College Department of Artificial Intelligence and Data Science Tamilnadu Chennai600123 India

ISBN: (纸本)9798350347791

In the year of 2019, a major destruction was raised to all over the Country called as Coronavirus, which is also termed as Covid-19. This is one of the serious threat and many people lost their lives due to the affection of Coronavirus disease. Due to the spread ratio and nature, nobody is interested to properly monitor the patients who are all affected with this disease. So, that a new technology is required to monitor the health conditions of patients and instantly reporting the details to the respective person and guardian as well. As technology advances, doctors are constantly on the lookout for new automated gadgets that might make it simpler to detect abnormalities in the human body. The Internet of Things makes it possible to create cutting-edge, transparent healthcare support structures. This paper introduced a new health monitoring scheme called Covid Patients Health Analyzer (CPHA), in which this methodology is associated with the help of trending technology called Internet of Things (IoT). This system introduces an Internet of Things assisted patient health surveillance technology, which aids medical professionals in remotely measuring the blood concentration, pulse rate, respiration level and temperature of the respective patient. Both the person's pulse rate and blood oxygen sufficiency levels are read using an intelligent sensor called MAX3100, whereas the individual's temperature is scanned with a powerful sensor called DS18B20. Using a humidity identification sensor, one may determine the relative humidity of a given space. All required information will be encoded and decoded by the ESP32 module before being processed. The patients Fingers are hooked up to sensors as well as readings are shown on a respective computer or smartphone. After putting the suggested system through its paces, the working is clearly tested and it delivers the desired results in good manner. By monitoring the process remotely, medical personnel no longer need to worry about c

关键词： Coronavirus

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Optimizing the light gradient-boosting machine algorithm for an efficient early detection of coronary heart disease

Informatics and Health

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Informatics and Health 2024年第2期1卷 70-81页

作者： Temidayo Oluwatosin Omotehinwa David Opeoluwa Oyewola Ervin Gubin Moung Department of Mathematics and Computer Science Federal University of Health SciencesP.M.B.145OtukpoNigeria Department of Mathematics and Statistics Federal University KashereP.M.B.0182GombeNigeria Data Technologies and Applications(DaTA)Research Group Faculty of Computing and InformaticsUniversiti Malaysia SabahKota Kinabalu 88400SabahMalaysia

Background:Coronary heart disease(CHD)remains a prominent cause of mortality globally,necessitating early and accurate detection *** diagnostic approaches can be invasive,costly,and time-consuming,necessitating the need for more efficient *** aimed to optimize the Light Gradient-Boosting Machine(LightGBM)algorithm to enhance its performance and accuracy in the early detection of CHD,providing a reliable,cost-effective,and non-invasive diagnostic ***:The Framingham Heart Study(FHS)dataset publicly available on Kaggle was used in this *** Imputations by Chained Equations(MICE)were applied separately to the training and testing sets to handle missing ***-SMOTE(Synthetic Minority Over-sampling Technique)was used on the training set to balance the *** LightGBM algorithm was selected for its efficiency in classification tasks,and Bayesian Optimization with Tree-structured Parzen Estimator(TPE)was employed to fine-tune its *** optimized LightGBM model was trained and evaluated using metrics such as accuracy,precision,and AUC-ROC on the test set,with cross-validation to ensure robustness and ***:The optimized LightGBM model showed significant improvement in early CHD *** baseline LightGBM model with dropped missing values had an accuracy of 0.8333,sensitivity of 0.1081,precision of 0.3429,F1 score of 0.1644,and AUC of *** MICE imputation,performance improved to an accuracy of 0.9399,sensitivity of 0.6693,precision of 0.9043,F1 score of 0.7692,and AUC of *** combined approach of Borderline-SMOTE,MICE imputation,and TPE for LightGBM achieved an accuracy of 0.9882,sensitivity of 0.9370,precision of 0.9835,F1 score of 0.9597,and AUC of 0.9963,indicating a highly effective and robust ***:The optimized model demonstrated outstanding performance in early CHD *** study’s strengths include its comprehensive approach to addres

关键词： Clinical decision making Coronary heart disease Light gradient-boosting machine Machine learning MICE Tree-structured Parzen estimator

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$ \tt {zkFL}$zkFL: Zero-Knowledge Proof-Based Gradient Aggregation for Federated Learning

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IEEE Transactions on Big data 2024年第2期11卷 447-460页

作者： Zhipeng Wang Nanqing Dong Jiahao Sun William Knottenbelt Yike Guo Department of Computing Imperial College London London U.K. Shanghai Artificial Intelligence Laboratory Shanghai China FLock.io London U.K. Department of Computer Science and Engineering Hong Kong University of Science and Technology Hong Kong SAR China Data Science Institute Imperial College London London U.K.

Federated learning (FL) is a machine learning paradigm, which enables multiple and decentralized clients to collaboratively train a model under the orchestration of a central aggregator. FL can be a scalable machine learning solution in Big data scenarios. Traditional FL relies on the trust assumption of the central aggregator, which forms cohorts of clients honestly. However, a malicious aggregator, in reality, could abandon and replace the client's training models, or insert fake clients, to manipulate the final training results. In this work, we introduce $ \tt {zkFL}$ , which leverages zero-knowledge proofs to tackle the issue of a malicious aggregator during the training model aggregation process. To guarantee the correct aggregation results, the aggregator provides a proof per round, demonstrating to the clients that the aggregator executes the intended behavior faithfully. To further reduce the verification cost of clients, we use blockchain to handle the proof in a zero-knowledge way, where miners (i.e., the participants validating and maintaining the blockchain data) can verify the proof without knowing the clients’ local and aggregated models. The theoretical analysis and empirical results show that $ \tt {zkFL}$ achieves better security and privacy than traditional FL, without modifying the underlying FL network structure or heavily compromising the training speed.

关键词： Blockchains Big data Training Computational modeling data models Privacy Arithmetic

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3D Based CNN for Improved Segmentation from the Hyperspectral Images

3D Based CNN for Improved Segmentation from the Hyperspectra...

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Computer science and Emerging Technologies (CSET), International Conference on

作者： Narmadha T Arockia Raj A P Saravanan A Suresh Kumar Ramesh Sekaran Gowri Shankar J School of Computer Science and Engineering Jain Deemed to be University Bengaluru Department of Computer Science and Engineering - Data Science Madanapalle Institute of Technology and Science Madanapalle India School of Computing Science and Engineering Galgotias University Delhi NCR

In this paper, we develop a 3D based CNN for Improved Segmentation of Paddy Fields from the HSI. Within the scope of this research, we will investigate a unique deep learning model, specifically 3D-CNN. In order to evaluate the validity of the model, it is applied to three separate datasets, each of which is then subjected to one of four distinct dimensionality reduction strategies. The purpose of this is to determine which of these four models is the most effective at reducing the number of dimensions. According to the findings, the 3D-CNN model is superior to both the AlexNet and CNN models in terms of the accuracy of its segmentations. In addition to this, the findings demonstrate that the 3D-CNN models perform better than the other approaches in terms of segmentation accuracy with a smaller number of datasets.

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A Comprehensive Study on the Application of Machine Learning Algorithms in the Prognosis of Ovarian Cancer

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Open Biomedical Engineering Journal 2023年 17卷

作者： Sindhu, S. Hemavathi, D. Sornalakshmi, K. Sujatha, G. Srividhya, S. Department of Data Science and Business Systems School of Computing SRM Institute of Science and Technology Tamil Nadu Kattankulathur 603203 India Department of Networking and Communications School of Computing SRM Institute of Science and Technology Tamil Nadu Kattankulathur 603203 India

Ovarian cancer is the third leading type of cancer found in women in India and ranks seventh globally. Several studies have shown that the population affected by ovarian cancer is profound to increase in the future. It is necessary to take steps for identifying cancer at the early stages to avoid mortality and recurrence. This chapter aims to survey the different ways machine learning models have been used in the prognosis of ovarian cancer-to predict the disease progression, recurrence, and mortality rate;analysis of genomic data sets;correlations and pattern analysis, and finding risk factors. The effective analytics on the imaging and other forms of data available from the patient’s electronic health records could unveil the possibilities of better or early diagnosis of ovarian cancer. The chapter will summarize the taxonomy of the various ways in which machine learning helps in ovarian cancer diagnosis, early detection, and treatment. In addition to surveying the current state-of-the-art application of machine learning algorithms for ovarian cancer diagnosis, the chapter aims to provide future research directions. © 2023 The Author(s). Published by Bentham science Publisher.

关键词： Cancer Correlation analysis Machine learning Ovarian cancer diagnosis Pattern analysis Prediction

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AI-based Framework for Discriminating Human-authored and AI-generated Text

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2024年

作者： Wani, Mudasir Ahmad Abd El-Latif, Ahmed A. Elaffendi, Mohammad Hussain, Amir Prince Sultan University EIAS Data Science Lab College of Computer and Information Sciences Center of Excellence in Quantum and Intelligent Computing Riyadh11586 Saudi Arabia Menoufia University Department of Mathematics and Computer Science Faculty of Science Shebin El-Koom32511 Egypt Edinburgh Napier University School of Computing Merchiston Campus EdinburghEH10 5DT United Kingdom

Deep learning techniques are increasingly adept at distinguishing between human-written and AI-generated text. This study presents a deep learning-based text classification system to discern the source of text - whether it's generated by AI or authored by a human. We compiled a comprehensive and diverse dataset incorporating existing datasets, real-time tweets, and synthetic data generated by cutting-edge AI models such as Google Bard and OpenAI's ChatGPT. Seven deep learning techniques including Convolutional Neural Networks (CNN), Artificial Neural Networks (ANN), Recurrent Neural Networks (RNN), Long Short-Term Memory networks (LSTM), Gated Recurrent Unit (GRU), Bidirectional LSTM (BiLSTM), and Bidirectional RNN (BiRNN) and four text embedding techniques namely One-hot, Word2Vec, TF-IDF, and BERT, are employed to perform 28 experiments (4∗7). Our findings exhibit outstanding performance across all models. BERT-based models notably outshine the others, with BERT-CNN achieving the highest accuracy of 98.87%, an MCC of 0.9742, and an F1 measure of 0.9916. Following closely, Bert-BiLSTM attained an accuracy, MCC, and F1 score of 98.77%, 0.9720, and 0.9909, respectively. This paper demonstrates the effectiveness of deep learning approaches in distinguishing between human and AI-generated text, with potential applications in automated content moderation, detection of AI-generated spam, and safeguarding the authenticity of user-generated content. © 2020 IEEE.

关键词： Convolutional neural networks

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