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arXiv 2024年

作者： Zhang, Xianzhi Zhou, Yipeng Hu, Miao Wu, Di Liao, Pengshan Guizani, Mohsen Sheng, Michael The School of Computer Science and Engineering Sun Yat-sen University Guangzhou China Guangdong Key Laboratory of Big Data Analysis and Processing Guangzhou China The Department of Computing Faculty of Science and Engineering Macquarie University Sydney Australia The Machine Learning Department Mohamed Bin Zayed University of Artificial Intelligence Abu Dhabi United Arab Emirates

To mitigate the rising concern about privacy leakage, the federated recommender (FR) paradigm emerges, in which decentralized clients co-train the recommendation model without exposing their raw user-item rating data. The differentially private federated recommender (DPFR) further enhances FR by injecting differentially private (DP) noises into clients. Yet, current DPFRs, suffering from noise distortion, cannot achieve satisfactory accuracy. Various efforts have been dedicated to improving DPFRs by adaptively allocating the privacy budget over the learning process. However, due to the intricate relation between privacy budget allocation and model accuracy, existing works are still far from maximizing DPFR accuracy. To address this challenge, we develop BGTplanner (Budget Planner) to strategically allocate the privacy budget for each round of DPFR training, improving overall training performance. Specifically, we leverage the Gaussian process regression and historical information to predict the change in recommendation accuracy with a certain allocated privacy budget. Additionally, Contextual Multi-Armed Bandit (CMAB) is harnessed to make privacy budget allocation decisions by reconciling the current improvement and long-term privacy constraints. Our extensive experimental results on real datasets demonstrate that BGTplanner achieves an average improvement of 6.76% in training performance compared to state-of-the-art baselines. Copyright © 2024, The Authors. All rights reserved.

关键词： Differential privacy

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A new computational model for quantifying blood flow dynamics across myogenically-active cerebral arterial networks

arXiv

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arXiv 2024年

作者： Coccarelli, Alberto Polydoros, Ioannis Drysdale, Alex Harraz, Osama F. Kadapa, Chennakesava Zienkiewicz Institute for Modelling Data and AI Faculty of Science and Engineering Swansea University Swansea United Kingdom Department of Mechanical Engineering Faculty of Science and Engineering Swansea University Swansea United Kingdom Department of Pharmacology Larner College of Medicine Vermont Center for Cardiovascular and Brain Health University of Vermont Burlington United States School of Computing Engineering and the Built Environment Edinburgh Napier University Edinburgh United Kingdom

Cerebral autoregulation plays a key physiological role by limiting blood flow changes in the face of pressure fluctuations. Although the involved cellular processes are mechanically driven, the quantification of haemodynamic forces in in-vivo settings remains extremely difficult and uncertain. In this work, we propose a novel computational framework for evaluating the blood flow dynamics across networks of myogenically active cerebral arteries, which can modulate their muscular tone to stabilize flow (and perfusion pressure) as well as to limit vascular intramural stress. The introduced framework is built on contractile (myogenically active) vascular wall mechanics and blood flow dynamics models, which can be numerically coupled in either a weak or strong way. We investigate the time dependency of the vascular wall response to pressure changes at both single vessel and network levels. The robustness of the model was assessed by considering different types of inlet signals and numerical settings in an idealized vascular network formed by a middle cerebral artery and its three generations. For the vessel size and boundary conditions considered, weak coupling ensured accurate results with a lower computational cost. To complete the analysis, we evaluated the effect of an upstream pressure surge on the haemodynamics of the vascular network. This provided a clear quantitative picture of how pressure and flow are redistributed across each vessel generation upon inlet pressure changes. This work paves the way for future combined experimental-computational studies aiming to decipher cerebral autoregulation. © 2024, CC BY.

关键词： Hemodynamics

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Network Access Selection for URLLC and eMBB Applications in Sub-6GHz-mmWave-THz Networks: Game Theory Versus Multi-Agent Reinforcement Learning

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IEEE Transactions on Communications 2024年

作者： Thanh Van, Nguyen Thi Le Tuan, Nguyen Luong, Nguyen Cong Nguyen, Tien Hoa Feng, Shaohan Gong, Shimin Niyato, Dusit In Kim, Dong Faculty of Electrical and Electronic Engineering PHENIKAA University Vietnam Hanoi12116 Viet Nam Faculty of Computer Science PHENIKAA University Hanoi12116 Viet Nam School of Electrical and Electronic Engineering Hanoi University of Science and Technology Hanoi100000 Viet Nam Zhejiang Gongshang University Hangzhou310018 China School of Intelligent Systems Engineering Sun Yat-sen University China College of Computing and Data Science Nanyang Technological University Singapore Department of Electrical and Computer Engineering Sungkyunkwan University Suwon16419 Korea Republic of

We investigate a heterogeneous network (HetNet) including sub-6GHz base stations (BSs), mmWave BSs, and THz BSs to support enhanced mobile broadband (eMBB) users and ultra-reliable low-latency communication (URLLC) users. We particularly investigate a user-centric network in which the users locally and dynamically select and switch among BSs over time to achieve their highest utility. Two types of users have different Quality of Service (QoS) requirements. Thus, we design two types of utility functions specifically for the eMBB users and URLLC users. Then, to model the dynamic selection behavior of the users, we propose to use a fractional game with the power-law memory. The fractional game allows the eMBB users and the URLLC users to incorporate their past strategies into their current selection, thus improving their utility. Furthermore, we consider the case that the BSs communicate the system state with each other, and we model the network selection of the users as a multi-agent problem. Then, we propose to use a multi-agent deep reinforcement learning (MADRL) algorithm that enables the URLLC users and eMBB users to make their network selection decision online to achieve their long-term utility. Various simulation results are provided to demonstrate the scalability and effectiveness of the proposed approaches. Particularly, compared with the classical game, the fractional game is able to achieve a higher utility but incurs a higher network adaptation cost. Moreover, the different types of URLLC users (in terms of latency and reliability requirements) and the number of URLLC users in the network significantly affect the total utility and the network selection strategies of the eMBB users. Importantly, given the full observations, the MADRL outperforms both classical and fractional games in terms of total network utility. © 2024 IEEE.

关键词： Deep reinforcement learning

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Dominic Welsh: his work and influence

arXiv

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arXiv 2024年

作者： Farr, Graham Mayhew, Dillon Oxley, James Department of Data Science and AI Faculty of IT Monash University ClaytonVIC3800 Australia School of Computing University of Leeds LeedsLS2 9JT United Kingdom Mathematics Department Louisiana State University Baton RougeLA70803-4918 United States

We review the work of Dominic Welsh (1938–2023), tracing his remarkable influence through his theorems, expository writing, students, and interactions. He was particularly adept at bringing different fields together and fostering the development of mathematics and mathematicians. His contributions ranged widely across discrete mathematics over four main career phases: discrete probability, matroids and graphs, computational complexity, and Tutte-Whitney polynomials. We give particular emphasis to his work in matroid theory and Tutte–Whitney *** Codes 01A70 (Primary) 05-03, 05B35, 05C31, 60K35, 68Q17, 68Q25 (Secondary) © 2024, CC BY.

关键词： Polynomials

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A Review of Breast Cancer Histopathological Image Classification 1

A Review of Breast Cancer Histopathological Image Classifica...

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1st International Conference on Material Processing and Technology, ICMProTech 2021

作者： Huang, Lei Zain, Azlan Mohd Zhou, Kai-Qing Chen, Chang-Feng School of Computing Faculty of Engineering Universiti Teknologi Malaysia UTM Skudai Johor Johor Bahru Malaysia UTM Big Data Centre School of Computing Universiti Teknologi Malaysia UTM Skudai Johor Johor Bahru Malaysia College of Information Science and Engineering Jishou University Jishou Hunan China

Breast Cancer (BC) is the most common malignant tumor for women in the world. Histopathological examination serves as basis for breast cancer diagnosis. Due to the low accuracy of histopathological images through manual judgment, the classification of histopathological images of breast cancer has become a research hotspot in the field of medical image processing. Accurate classification of images can help doctors to properly diagnoses and improve the survival rate of patients. This paper reviews the existing works on histopathological image classification of breast cancer and analysis the advantages and disadvantages of related algorithms. Findings of the histopathological image classification of the Breast Cancer study are drawn, and the possible future directions are also discussed. © 2021 Institute of Physics Publishing. All rights reserved.

关键词： Image classification

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Modeling of toddler stunting in the province of east nusa tenggara using multivariate adaptive regression splines (mars) method 5

Modeling of toddler stunting in the province of east nusa te...

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5th International Conference on Mathematics: Pure, Applied and Computation, ICoMPAC 2019

作者： Azizah, D.M. Permatasari, E.O. Statistics Department Faculty of Mathematics Computing and Data Science InstitutTeknologiSepuluhNopember GebangKidul Road number 66 Surabaya60111 Indonesia

Stunting is a condition of failure to thrive in children under five (babies under five years old) as a result of chronic malnutrition so that the child is too short for his age. Malnutrition occurs since the baby is in the womb and in the early period after the baby is born. However, the condition of stunting only appears after the baby is 2 years old. Based on Basic Health Research in 2017 the province with the highest prevalence of short and very short toddlers in Indonesia is East Nusa Tenggara which is equal to 40.3 percent. This shows that the prevalence of stunting toddlers in NTT Province is still far from the overall prevalence of stunting toddlers in Indonesia. Cases of toddler stunting can be analyzed using the Multivariate Adaptive Regression Splines (MARS) method because the data used are high-dimensional data and do not show clear patterns of relationships between response variables and predictor variables. The data used is the percentage of stunting toddlers in NTT in 2017 with the district / city research unit. Based on the analysis, it was found that the district in NTT with the highest percentage of stunting children was South East Timor and the district with the lowest percentage was East Manggarai. The best MARS model is formed from 3 basis functions with 2 predictor variables included in the model, namely the percentage of pregnant women at risk of chronic lack of energy (X1) and the percentage of infants with low birth weight (X6). And after the classification results obtained the accuracy of the classification of 77.27% with an error of 22.72%. © Published under licence by IOP Publishing Ltd.

关键词： Clustering algorithms

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A Study of Deep Learning Models for Identifying and Estimating Psychological Stress and Disorders Using Electroencephalogram Signals

A Study of Deep Learning Models for Identifying and Estimati...

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13th International Electrical Engineering Congress, iEECON 2025

作者： Lakshmi Balaji, R.S. Batumalay, M. Yingthawornsuk, Thaweesak Swamy, Avanthika Suwannakhun, Sirimonpak Department of Advanced Computing Sciences Chennai India Inti International University Faculty of Data Science and Information Technology Nilai Malaysia King Mongkut's University of Technology Thonburi Department of Media Technology Bangkok Thailand Compliance Administrator Birmingham City Council United Kingdom King Mongkut's University of Technology Thonburi Faculty of Industrial Education and Technology Bangkok Thailand

ISBN: (纸本)9798331543952

The identification of psychological stress and disorders like panic attacks and anxiety is vital for improving mental health care. This study investigates the use of electroencephalogram (EEG) signals to predict these conditions, leveraging three deep learning models: a feedforward neural network, a transformer-based model, and a hybrid convolutional neural network (CNN)-long short-term memory network (LSTM). Performance metrics, including accuracy, F1 score, precision, recall, RMSE, and R-squared, were used for evaluation. The feedforward model achieved accuracies of 95.42% and 95.66% for predicting panic and anxiety attacks, respectively, while the transformer-based model also delivered promising results. Notably, the CNN-LSTM hybrid model outperformed the others, achieving 95.90% accuracy for anxiety prediction and excelling in stress level estimation with an RMSE of 0.1671 and an R-squared value of 0.9438. These findings demonstrate the hybrid model's efficacy in predicting psychological conditions and estimating stress levels. This approach holds significant promise for improving mental health assessments, early interventions, and treatment strategies. © 2025 IEEE.

关键词： Health insurance

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Optimizing Ethanol Production in Escherichia Coli Using a Hybrid of Particle Swarm Optimization and Artificial Bee Colony 22

Optimizing Ethanol Production in Escherichia Coli Using a Hy...

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Proceedings of the 6th International Conference on Advances in Artificial Intelligence

作者： Mohamad Faiz Dzulkalnine Mohd Saberi Mohamad Yee Wen Choon Muhammad Akmal Remli Hany Alashwal Artificial Intelligence and Bioinformatics Research Group Faculty of Computing Universiti Teknologi Malaysia Malaysia Health Data Science Lab Department of Genetics and Genomics College of Medical and Health Sciences United Arab Emirates University UAE and Big Data Analytics Center United Arab Emirates University UAE Institute for Artificial Intelligence and Big Data Universiti Malaysia Kelantan Malaysia and Department of Data Science Universiti Malaysia Kelantan Malaysia Department of Computer Science and Software Engineering College of Information Technology United Arab Emirates University UAE and Big Data Analytics Center United Arab Emirates University UAE

ISBN: (纸本)9781450396943

Metabolic engineering for biomass production using microorganisms’ cell has received considerable attention in recent years. This is due to the biomass products being extensively used in the field of food additives, supplements, pharmaceuticals, and polymer materials. In this paper, ethanol production in Escherichia coli (E. coli) is the desired product. Sugarcane and corn are often used to produce ethanol. However, one of the problems to produce adequate amounts of ethanol is that large areas are needed to plant sugarcane and corn. Furthermore, the amount of time for the process of dry milling and wet milling is high, which are 40 to 50 hours and 24 to 48 hours, respectively. The wet laboratory is also having limitation on the production of ethanol in microorganisms because the amount of the ethanol produced is not satisfying. Hence, a lot of metabolic engineering techniques is introduced to enhance the production of ethanol in E. coli, such as gene knockout strategy, but the production is yet to meet the demand. Therefore, this paper proposes a hybrid algorithm of Particle Swarm Optimization with the Artificial Bee Colony algorithm (PSOABC) to identify the optimal set of gene knockout strategy to improve the ethanol production in E. coli. A list of genes to knockout, production of the desired product, and growth rate are presented in this paper. PSOABC has shown better performance in terms of production, growth rate and accuracy.

关键词： Gene Knockouts Particle Swarm Optimization Artificial Bee Colony Ethanol Flux Balance Analysis

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Multi-Channel Hypergraph Convolution Group Recommendation with Member Information Enhancement 25

Multi-Channel Hypergraph Convolution Group Recommendation wi...

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25th IEEE International Conferences on High Performance computing and Communications, 9th International Conference on data science and Systems, 21st IEEE International Conference on Smart City and 9th IEEE International Conference on Dependability in Sensor, Cloud and Big data Systems and Applications, HPCC/DSS/SmartCity/DependSys 2023

作者： Chen, Tianhao Gao, Qian Fan, Jun Qilu University of Technology Shandong Academy of Sciences Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center Shandong Jinan250014 China Qilu University of Technology Shandong Academy of Sciences Shandong Engineering Research Center of Big Data Applied Technology Faculty of Computer Science and Technology Shandong Jinan250353 China Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer Science Shandong Jinan250014 China China Telecom Digital Intelligence Techonology Co Ltd Shandong Jinan250101 China

ISBN: (纸本)9798350330014

Group recommendation involves comprehensively considering various aspects, including members and items, to predict the overall interests of a group and recommend suitable items through a recommendation system. With the rapid evolution of the Internet, online group activities have become increasingly prevalent, making group recommendation a highly discussed topic within the realm of recommendation systems. However, current research in group recommendation still confronts the following challenges. Firstly, a predominant portion of group recommendation research focuses solely on aggregating group-level information, neglecting the valuable contribution of higher-order member-level insights to group recommendation. Moreover, some aggregation strategies overly prioritize fairness, thereby disregarding common real-world patterns. Secondly, previous studies have primarily relied on aggregating individual member interests to establish group preferences, lacking a holistic consideration of group dynamics. To tackle these challenges, this study introduces an innovative approach by implementing multi-channel hypergraph convolution within the member perspective. This approach aims to effectively extract higher-order insights from members by utilizing a member information enhancement module. By establishing group interconnections based on similarity and employing an adaptive fusion network to amalgamate multiple viewpoints, a final representation is derived. Experimental results demonstrate that our proposed model surpasses baseline models by 3% to 4% in terms of hit rate and accuracy, validating the efficacy of our approach. © 2023 IEEE.

关键词： Recommender systems

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A New Interactive Gaming Approach for Enhancing Math Learning Among Marginalized Communities

A New Interactive Gaming Approach for Enhancing Math Learnin...

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Smart Technology (ICE-SMARTec), IEEE International Conference on

作者： Hapini Awang Nur Suhaili Mansor Nor Hazlyna Harun Juhaida Abu Bakar Abdulrazak F. Shahatha Al-Mashhadani Mohamad Ghozali Hassan Institute for Advanced and Smart Digital Opportunities School of Computing Universiti Utara Malaysia Sintok Malaysia Data Science Research Lab School of Computing Universiti Utara Malaysia Sintok Malaysia Faculty of Business Sohar University Sohar Oman Disaster Management Institute School of Technology Management and Logistics Universiti Utara Malaysia Sintok Malaysia

Mathematics is often considered a boring and challenging subject, which makes it difficult for educators to attract marginalized children to learn and embrace it. Therefore, this article discusses the development and evaluation of a mobile game application called Math-Hero, designed to enhance marginalized children's mathematical skills through an interactive, engaging, and heartfelt learning environment. To examine the effectiveness of Math-Hero, an online survey method was employed to collect data from 32 respondents. The evaluation model was developed following the Technology Acceptance Model (TAM). Three constructs - Perceived Usefulness (PU), Perceived Ease of Use (PEOU), and Intention to Adopt (ITA) - were examined quantitatively using descriptive statistics and correlation analysis. Additionally, qualitative data were analyzed using thematic and sentiment analyses. The results of quantitative analyses indicate that Math-Hero is easy to use, beneficial, and the users intend to use it in the future. Its ease of use has a positive correlation with usefulness and intention to adopt it. The sentiment analysis also supports the findings, as most of the users have a positive and neutral perception of Math-Hero. Although Math-Hero has shown promise in improving marginalized children's mathematical skills, there is still room for improvement to make it even more robust and effective in the future. Moreover, further research is necessary to empirically demonstrate the effectiveness of Math-Hero among diverse marginalized communities, beyond just Semai youngsters.

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