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Closed-Form Models of Accuracy Loss due to Subsampling in SVD Collaborative Filtering

Big data Mining and Analytics 2023年第1期6卷 72-84页

作者： Samin Poudel Marwan Bikdash Department of Computational Data Science and Engineering North Carolina A&T State UniversityGreensboroNC 27401USA

We postulate and analyze a nonlinear subsampling accuracy loss(SSAL)model based on the root mean square error(RMSE)and two SSAL models based on the mean square error(MSE),suggested by extensive preliminary *** SSAL models predict accuracy loss in terms of subsampling parameters like the fraction of users dropped(FUD)and the fraction of items dropped(FID).We seek to investigate whether the models depend on the characteristics of the dataset in a constant way across datasets when using the SVD collaborative filtering(CF)*** dataset characteristics considered include various densities of the rating matrix and the numbers of users and *** simulations and rigorous regression analysis led to empirical symmetrical SSAL models in terms of FID and FUD whose coefficients depend only on the data *** SSAL models came out to be multi-linear in terms of odds ratios of dropping a user(or an item)*** dropping ***,one MSE deterioration model turned out to be linear in the FID and FUD odds where their interaction term has a zero *** importantly,the models are constant in the sense that they are written in closed-form using the considered data characteristics(densities and numbers of users and items).The models are validated through extensive simulations based on 850 synthetically generated primary(pre-subsampling)matrices derived from the 25M MovieLens *** 460000 subsampled rating matrices were then simulated and subjected to the singular value decomposition(SVD)CF *** validation was conducted using the 1M MovieLens and the Yahoo!Music Rating *** models were constant and significant across all 3 datasets.

关键词： collaborative filtering subsampling accuracy loss models performance loss recommendation system simulation rating matrix root mean square error

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A Tutorial on Federated Learning from Theory to Practice:Foundations,Software Frameworks,Exemplary Use Cases,and Selected Trends

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IEEE/CAA Journal of Automatica Sinica 2024年第4期11卷 824-850页

作者： M.Victoria Luzón Nuria Rodríguez-Barroso Alberto Argente-Garrido Daniel Jiménez-López Jose M.Moyano Javier Del Ser Weiping Ding Francisco Herrera Department of Software Engineering Andalusian Research Institute in Data Science and Computational Intelligence(DaSCI)University of GranadaGranada 18071Spain Department of Computer Science and Artificial Intelligence Andalusian Research Institute in Data Science and Computational Intelligence(DaSCI)University of GranadaGranada 18071Spain Department of Communications Engineering University of the Basque Country(UPV/EHU)and also with TECNALIABasque Research&Technology Alliance(BRTA)Spain School of Information Science and Technology Nantong UniversityNantong 226019China

When data privacy is imposed as a necessity,Federated learning(FL)emerges as a relevant artificial intelligence field for developing machine learning(ML)models in a distributed and decentralized *** allows ML models to be trained on local devices without any need for centralized data transfer,thereby reducing both the exposure of sensitive data and the possibility of data interception by malicious third *** paradigm has gained momentum in the last few years,spurred by the plethora of real-world applications that have leveraged its ability to improve the efficiency of distributed learning and to accommodate numerous participants with their data *** virtue of FL,models can be learned from all such distributed data sources while preserving data *** aim of this paper is to provide a practical tutorial on FL,including a short methodology and a systematic analysis of existing software ***,our tutorial provides exemplary cases of study from three complementary perspectives:i)Foundations of FL,describing the main components of FL,from key elements to FL categories;ii)Implementation guidelines and exemplary cases of study,by systematically examining the functionalities provided by existing software frameworks for FL deployment,devising a methodology to design a FL scenario,and providing exemplary cases of study with source code for different ML approaches;and iii)Trends,shortly reviewing a non-exhaustive list of research directions that are under active investigation in the current FL *** ultimate purpose of this work is to establish itself as a referential work for researchers,developers,and data scientists willing to explore the capabilities of FL in practical applications.

关键词： data privacy distributed machine learning federated learning software frameworks

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Large language models design sequencedefined macromolecules via evolutionary optimization

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npj computational Materials 2024年第1期10卷 446-453页

作者： Wesley F.Reinhart Antonia Statt Department of Materials Science and Engineering Pennsylvania State UniversityUniversity Park16802 PAUSA Institute for Computational and Data Sciences Pennsylvania State UniversityUniversity Park16802 PAUSA Department of Materials Science and Engineering Grainger College of EngineeringUniversity of Illinois Urbana-ChampaignChampaign61801 ILUSA

We demonstrate the ability of a large language model to perform evolutionary optimization for materials ***’s Claude 3.5 model outperforms an active learning scheme with handcrafted surrogate models and an evolutionary algorithm in selecting monomer sequences to produce targeted morphologies in macromolecular *** pre-trained language models can potentially reduce the need for hyperparameter tuning while offering new capabilities such as *** model performs this task effectively with orwithout context about the task itself,but domain-specific context sometimes results in faster convergence to good ***,when this context is withheld,the model infers an approximate notion of the task(e.g.,calling it a protein folding problem).This work provides evidence of Claude 3.5’s ability to act as an evolutionary optimizer,a recently discovered emergent behavior of large language models,and demonstrates a practical use case in the study and design of soft materials.

关键词： offering evolutionary faster

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Optimization Models for Teaching Load Formation of University Teachers 1

Optimization Models for Teaching Load Formation of Universit...

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IEEE AITU: 1st International Student Conference: "Digital Generation", AITU 2024

作者： Chigambayeva, Diana Goryakin, Makar Batova, Polina Department of Computational and Data Science Astana IT University Astana Kazakhstan Depatment of Computer Engineering Astana IT University Astana Kazakhstan

ISBN: (纸本)9798350364378

Functional and mathematical models for the distribution of academic workload at the stage of preparing the educational process at a university are considered, which make it possible to largely determine the uniformity of teachers’ workload with various types of academic work when fulfilling the criteria and restrictions adopted for the distribution. ©2024 IEEE.

关键词： functional model mathematical model study load educational process educational work types of educational work

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Evaluating Power System Stability in PV-Integrated Grid with AI-Based Solar Forecasting Model

Evaluating Power System Stability in PV-Integrated Grid with...

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2025 IEEE SoutheastCon, SoutheastCon 2025

作者： Darwish, Hiba Harb, Ahmad Gokaraju, Balakrishna Bikdash, Marwan North Carolina A&T State University Computational Data Science and Engineering Department Greensboro United States

ISBN: (纸本)9798331504847

This research focuses on forecasting solar irradiance for the year 2025 by applying Artificial Neural Network (ANN) based on historical data collected over three years (2022-2024) for the humidity, earth temperature, and wind speed. The primary aim is to assess the potential output power of a photovoltaic (PV) system and its reliability in covering electrical loads. The developed forecasting model demonstrated promising accuracy, achieving accepted values for the Mean Squared Error (MSE). To evaluate the system's performance under adverse conditions, two fault scenarios were simulated by MATLAB-Simulink software: one where the load was supplied entirely by the grid (no PV system), and another where both the grid and the PV system supplied the load. Results indicated that the inclusion of the PV system introduced challenges in system recovery due to its lack of inertia, exacerbating instability during faults. To address this limitation, a battery energy storage system was integrated into the PV setup. This addition significantly improved the system's resilience and ensured smooth recovery once the fault is removed. The study underscores the importance of incorporating energy storage solutions to enhance the reliability and stability of PV-based power systems, offering critical insights for designing sustainable and robust energy infrastructures. © 2025 IEEE.

关键词： MATLAB

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Asynchrony-Tolerant Schemes to Enhance Scalability of High-Order Compressible Flow Solver 31

Asynchrony-Tolerant Schemes to Enhance Scalability of High-O...

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31st IEEE International Conference on High Performance Computing, data, and Analytics Workshops, HiPCW 2024

作者： Aswin Kumar, A. Vadlamani, Nagabhushana Rao Aditya, Konduri Department of Aerospace Engineering Indian Institute of Technology Madras Chennai India Department of Computational and Data Sciences Indian Institute of Science Bengaluru India

ISBN: (纸本)9798331509118

High-order asynchrony-tolerant (AT) schemes are used to solve the compressible Navier-Stokes equations. The AT schemes are validated for accuracy using canonical flow problems and are found to be in excellent agreement with the standard schemes. Strong and weak scaling analyses are performed, and significant speedups (up to 3.5×) are observed for the asynchronous algorithms compared to the standard synchronous algorithm. © 2024 IEEE

关键词： Navier Stokes equations

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Joint Wireless and Computing Resource Management with Optimal Slice Selection in in-network-edge Metaverse System 15

Joint Wireless and Computing Resource Management with Optima...

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15th International Conference on Information and Communication Technology Convergence, ICTC 2024

作者： Rashid, Sulaiman Muhammad Aliyu, Ibrahim Isah, Abubakar Lee, Jihoon Oh, Sangwon Hahn, Minsoo Kim, Jinsul Chonnam National University Department of Intelligent Electronics and Computer Engineering Gwangju Korea Republic of Astana It University Department of Computational and Data Science Astana Kazakhstan

ISBN: (纸本)9798350364637

This paper presents an approach to joint wireless and computing resource management in slice-enabled metaverse networks, addressing the challenges of inter-slice and intra-slice resource allocation in the presence of in-network computing. We formulate the problem as a mixed-integer nonlinear programming (MINLP) problem and derive an optimal solution using standard optimization techniques. Through extensive simulations, we demonstrate that our proposed method significantly improves system performance by effectively balancing the allocation of radio and computing resources across multiple slices. Our approach outperforms existing benchmarks, particularly in scenarios with high user demand and varying computational tasks. © 2024 IEEE.

关键词： Mixed-integer linear programming

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Transfer Learning and Fine-tuned CNN Architecture for Dog Breed Classification 3

Transfer Learning and Fine-tuned CNN Architecture for Dog Br...

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3rd International Conference on Artificial Intelligence For Internet of Things, AIIoT 2024

作者： Gunaranjan, K. Vijay, K. Naveen, P. St. Joseph's College of Engineering Department of Artificial Intelligence and Data Science Chennai India SRM institute of science and technology Department of computational intelligence Chennai India

ISBN: (纸本)9798350372120

This study presents a comprehensive approach to dog breed classification, leveraging transfer learning algorithms and Convolutional Neural Networks (CNNs). The dataset utilized for training and evaluation is sourced from the Dog Breed Identification dataset, encompassing 120 distinct breeds. A pre-trained CNN model is employed, and through the application of transfer learning, the proposed models achieve an impressive accuracy rate of 99%. The primary objective is to enable users to submit images of dogs, with the model accurately identifying the specific breed from the diverse dataset. The research underscores the efficacy of transfer learning and pretrained models in achieving exceptional accuracy for the nuanced task of dog breed classification, contributing to the advancements in image recognition within the context of diverse canine breeds. © 2024 IEEE.

关键词： Convolutional neural networks

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Optimal Dependence of Performance and Efficiency of Collaborative Filtering on Random Stratified Subsampling

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Big data Mining and Analytics 2022年第3期5卷 192-205页

作者： Samin Poudel Marwan Bikdash Department of Computational Data Science and Engineering North Carolina A&T State UniversityGreensboroNC 27401USA

Dropping fractions of users or items judiciously can reduce the computational cost of Collaborative Filtering(CF)*** effect of this subsampling on the computing time and accuracy of CF is not fully understood,and clear guidelines for selecting optimal or even appropriate subsampling levels are not *** this paper,we present a Density-based Random Stratified Subsampling using Clustering(DRSC)algorithm in which the desired Fraction of Users Dropped(FUD)and Fraction of Items Dropped(FID)are specified,and the overall density during subsampling is ***,we develop simple models of the Training Time Improvement(TTI)and the Accuracy Loss(AL)as functions of FUD and FID,based on extensive simulations of seven standard CF algorithms as applied to various primary matrices from MovieLens,Yahoo Music Rating,and Amazon Automotive *** show that both TTI and a scaled AL are bi-linear in FID and FUD for all seven *** TTI linear regression of a CF method appears to be same for all *** simulations illustrate that TTI can be estimated reliably with FUD and FID only,but AL requires considering additional dataset *** derived models are then used to optimize the levels of subsampling addressing the tradeoff between TTI and AL.A simple sub-optimal approximation was found,in which the optimal AL is proportional to the optimal Training Time Reduction Factor(TTRF)for higher values of TTRF,and the optimal subsampling levels,like optimal FID/(1-FID),are proportional to the square root of TTRF.

关键词： Collaborative Filtering(CF) subsampling Training Time Improvement(TTI) performance loss Recommendation System(RS) collaborative filtering optimal solutions rating matrix

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Building a portable parallel asynchronous PDE solver using Kokkos 31

Building a portable parallel asynchronous PDE solver using K...

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31st IEEE International Conference on High Performance Computing, data, and Analytics Workshops, HiPCW 2024

作者： Bhat, Ranjan Aditya, Konduri Department of Computer Science and Engineering Manipal Institute of Technology Manipal Academy of Higher Education Manipal India Department of Computational and Data Sciences Indian Institute of Science Bengaluru India

ISBN: (纸本)9798331509118

In this study, we outline the design and implementation of a portable massively parallel asynchronous solver for time-dependent partial differential equations (PDEs). The solver is implemented using Kokkos library for portability across different node architectures and is coupled with the communication-avoiding algorithm that relaxes the communication across nodes, thus improving the scalability. Asynchrony-tolerant finite difference schemes for spatial derivatives along with the low storage Runge-Kutta scheme for time integration are used to achieve high-order accuracy. The efficacy of the implementation is demonstrated using a mini-app that solves the three-dimensional diffusion problem. © 2024 IEEE

关键词： Scalability

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