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Pervasive Computational Technologies (ICPCT), International Conference on

作者： Yojna Arora Neha Gupta Yogesh Singh Rathore Nidhi Bansal Aina Mehta Shikha Chadha Ashwani Kumar Department of Computer Science and Engineering Sharda University Greater Noida India School of Computer Science & Information Technology Symbiosis University of Applied Sciences Indore Madhya Pradesh India Department of Computer Science and Applications School of Engineering and Technology Sharda University Greater Noida Uttar Pradesh India Department of Computer Science & Engineering School of Engineering and Technology Manav Rachna International Institute of Research and Studies (Deemed to be University) Faridabad Haryana India School of Computer Science Engineering and Technology Bennett University India

ISBN: (数字)9798331508685

ISBN: (纸本)9798331519476

Recently, with increased use of mobile phones, it has transformed into a multibillion-dollar Short Message Service or SMS. However, the drop in the cost of messaging services has led to an increased number of unsolicited commercial messages, or spam, being delivered to mobile phones. Several reasons exist for which traditional algorithms used in email filtering may not efficiently classify the spam in SMS such as the absence of strong SMS spam databases, with the short length and features of the message, the informal language used, etc. This paper uses a real SMS spam database After preprocessing the data and extraction of relevant features, Naïve Bayes machine learning techniques is applied to get the results. Then it is analyzed using confusion matrix, accuracy, precision and recall values. A learning curve with clearly defined relationship between the training and testing data set and overall error at the end.

关键词： Training Machine learning algorithms Databases Unsolicited e-mail Message services Feature extraction Mobile handsets Tokenization Bayes methods Testing

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AI Agents for computer Use: A Review of Instruction-based computer Control, GUI Automation, and Operator Assistants

arXiv

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arXiv 2025年

作者： Sager, Pascal J. Meyer, Benjamin Yan, Peng von Wartburg-Kottler, Rebekka Etaiwi, Layan Enayati, Aref Nobel, Gabriel Abdulkadir, Ahmed Grewe, Benjamin F. Stadelmann, Thilo Centre for Artificial Intelligence Zurich University of Applied Sciences Winterthur Switzerland Institute of Neuroinformatics University of Zurich ETH Zurich Zurich Switzerland Department of Computer Engineering and Software Engineering Polytechnique Montreal MontrealQC Canada Institute of Applied Mathematics and Physics Zurich University of Applied Sciences Winterthur Switzerland European Centre for Living Technology Venice Italy

Instruction-based computer control agents (CCAs) execute complex action sequences on personal computers or mobile devices to fulfill tasks using the same graphical user interfaces as a human user would, provided instructions in natural language. This review offers a comprehensive overview of the emerging field of instruction-based computer control, examining available agents – their taxonomy, development, and respective resources – and emphasizing the shift from manually designed, specialized agents to leveraging foundation models such as large language models (LLMs) and vision-language models (VLMs). We formalize the problem and establish a taxonomy of the field to analyze agents from three perspectives: (a) the environment perspective, analyzing computer environments;(b) the interaction perspective, describing observations spaces (e.g., screenshots, HTML) and action spaces (e.g., mouse and keyboard actions, executable code);and (c) the agent perspective, focusing on the core principle of how an agent acts and learns to act. Our framework encompasses both specialized and foundation agents, facilitating their comparative analysis and revealing how prior solutions in specialized agents, such as an environment learning step, can guide the development of more capable foundation agents. Additionally, we review current CCA datasets and CCA evaluation methods and outline the challenges to deploying such agents in a productive setting. In total, we review and classify 86 CCAs and 33 related datasets. By highlighting trends, limitations, and future research directions, this work presents a comprehensive foundation to obtain a broad understanding of the field and push its future development. © 2025, CC BY.

关键词： Taxonomies

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Photovoltaic Module Parameter Estimation by Using Wild Horse Optimizer

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Procedia computer Science 2025年 258卷 2808-2816页

作者： Anuj Kumar Tina Sharma Manoj K. Singh Sangeeta Pant Hitesh Kumar Sharma School of Computer Science Engineering & Applications D Y Patil International University (DYPIU) Pune India- 411044 Department of Mathematics Birla Institute of Technology and Science Pilani India School of Computer Science Engineering and Technology Bennett University Greater Noida India- 201310 Department of Applied Sciences Symbiosis Institute of Technology Symbiosis International (Deemed University) (SIU) Pune India- 412115 School of Computer Science University of Petroleum & Energy Studies Dehradun India-248007

The development of highly accurate models is essential for optimizing the performance and efficiency of photovoltaic (PV) modules, which are integral to renewable energy systems. In this context, metaheuristic algorithms have become indispensable tools for tackling complex engineering challenges, particularly in the areas of evaluation, simulation, and parameter estimation. These algorithms excel at finding optimal or near-optimal solutions for problems where traditional methods may struggle due to their complexity or computational intensity. This article presents a novel framework for estimating the parameters of PV modules using the Wild Horse Optimizer (WHO), a recently introduced metaheuristic algorithm known for its robustness and efficiency. Specifically, the framework is applied to the Double Diode Model (DDM) of a solar cell, a widely recognized model for representing the electrical behavior of PV modules. The simulation results clearly illustrate that the proposed framework based on WHO algorithm offers significant improvements in the accuracy and effectiveness of parameter estimation for PV modules, compared to Artificial Bee Swarm Optimization (ABSO), the Flower Pollination Algorithm (FPA), and several others, thereby validating the proposed approach.

关键词： Optimization Photovoltaic (PV) modules Double diode model (DDM) metaheuristics Wild Horse Optimizer (WHO)

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Quantum permutation puzzles with indistinguishable particles

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Physical Review A 2025年第4期111卷 042419-042419页

作者： Noah Lordi Maedée Trank-Greene Akira Kyle Joshua Combes Department of Physics University of Colorado Boulder 2000 Colorado Avenue Boulder Colorado 80309 USA Department of Applied Mathematics University of Colorado Boulder Boulder Colorado 80309 USA Electrical Computer and Energy Engineering University of Colorado Boulder Boulder Colorado 80309 USA Schools of Physics and Mathematics University of Melbourne Melbourne Victoria 3010 Australia

Permutation puzzles, such as the Rubik's Cube and the 15 puzzle, are enjoyed by the general public and mathematicians alike. Here we introduce quantum versions of permutation puzzles where the pieces of the puzzles are replaced with indistinguishable quantum particles. The moves in the puzzle are achieved by swapping or permuting the particles. We show that simply permuting the particles can be mapped to a classical permutation puzzle, even though the identical particles are entangled. However, we obtain a genuine quantum puzzle by adding a quantum move: the square root of swap. The resulting puzzle cannot be mapped to a classical permutation puzzle. We focus predominately on the quantization of the 2×2 slide puzzle and briefly treat the 2×2×1 Rubik's Cube.

关键词： Quantum entanglement

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Multiaccuracy and Multicalibration via Proxy Groups

arXiv

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arXiv 2025年

作者： Bharti, Beepul Clemens-Sewall, Mary Versa Yi, Paul H. Sulam, Jeremias Johns Hopkins University United States Department of Biomedical Engineering Johns Hopkins University United States Department of Applied Mathematics & Statistics Johns Hopkins University United States St. Jude Children’s Research Hospital United States Department of Computer Science Johns Hopkins University United States

As the use of predictive machine learning algorithms increases in high-stakes decision-making, it is imperative that these algorithms are fair across sensitive groups. Unfortunately, measuring and enforcing fairness in real-world applications can be challenging due to missing or incomplete sensitive group data. Proxy-sensitive attributes have been proposed as a practical and effective solution in these settings, but only for parity-based fairness notions. Knowing how to evaluate and control for fairness with missing sensitive group data for newer and more flexible frameworks, such as multiaccuracy and multicalibration, remains unexplored. In this work, we address this gap by demonstrating that in the absence of sensitive group data, proxy-sensitive attributes can provably be used to derive actionable upper bounds on the true multiaccuracy and multicalibration, providing insights into a model’s potential worst-case fairness violations. Additionally, we show that adjusting models to satisfy multiaccuracy and multicalibration across proxy-sensitive attributes can significantly mitigate these violations for the true, but unknown, sensitive groups. Through several experiments on real-world datasets, we illustrate that approximate multiaccuracy and multicalibration can be achieved even when sensitive group information is incomplete or unavailable. © 2025, CC BY.

关键词： Sensitive data

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Analytic solutions of the time-fractional Boiti-Leon-Manna-Pempinelli equation via novel transformation technique

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Scientific Reports 2025年第1期15卷 1-15页

作者： Yasmeen, Bushra Ahmad, Khalil Akgül, Ali Al-Mdallal, Qasem Department of Mathematics Faculty of Basic and Applied Sciences Air University PAF Complex E-9 Islamabad 44000 Pakistan Department of Electronics and Communication Engineering Saveetha School of Engineering SIMATS Tamilnadu Chennai India Art and Science Faculty Department of Mathematics Siirt University Siirt 56100 Turkey Department of Computer Engineering Biruni University Topkapi Istanbul 34010 Turkey Department of Mathematics Mathematics Research Center Near East University Near East Boulevard Nicosia /Mersin 10 PC: 99138 Turkey Department of Mathematical Sciences UAE University P.O. Box 17551 Al-Ain United Arab Emirates Applied Science Research Center Applied Science Private University Amman 11937 Jordan

This paper presents new analytical solutions for the time-fractional Boiti-Leon-Manna-Pempinelli (BLMP) equation, a crucial model for physical phenomena. Our approach yields novel wave solutions, whose propagation and dynamics are examined for diverse parameter values. The obtained solutions contain rational and natural logarithm functions. The graphical representations of the attained solutions are represented by plotted graphs with suitable parameters. The plotted graphs show different solitons and nonlinear wave solutions. The examination of these solutions involves a comprehensive analysis of their propagation and dynamics through analytic techniques. Our results with existing literature and found that our approach yields more accurate and efficient solutions. The novelty of these solutions is essential for understanding nonlinear behavior and natural phenomena. By developing analytical methods for nonlinear equations, this work advances our knowledge of complex systems. The results provide valuable insights into the equation’s behavior, shedding light on the underlying physical mechanisms. This research contributes to the development of analytical methods for nonlinear equations, fostering future research in the field. The findings are relevant to various areas of physics, including wave dynamics and nonlinear systems. © The Author(s) 2025.

关键词： Analytic solution Boiti-Leon-Manna-Pempinelli Euler’s second order linear ODE Fractional calculus New variable transformations Time-fractional Boiti-Leon-Manna-Pempinelli Time-fractional derivative

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Predicting Power Output of Solar Photovoltaic Panels Using Machine Learning Techniques

Predicting Power Output of Solar Photovoltaic Panels Using M...

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Artificial Intelligence, Applications, Innovation and Ethics (AI2E), International Conference for

作者： Karimulla Syed Mohammad Ameer Hamza Yousuf Murali Krishna Boddu Sainadh Singh K Mahaboob Shareef Syed Afaq Ahmad Engineering Department University of Technology and Applied science-Shinas Shinas Sultanate of Oman Department of EEE BVRIT Narsapur India Vignan's Lara ITS India Department of Mathematics and Computer Science Modern College of Bussiness & Science Muscat Sultanate of Oman

ISBN: (数字)9798331513733

ISBN: (纸本)9798331513740

The prediction of solar power generation is essential for effective integration of renewable energy into power grids, aiding in grid stability, energy planning, and efficient resource allocation. Due to the inherent variability of solar energy caused by factors like weather patterns, time of day, and seasonal changes, machine learning (ML) has appeared as a powerful tool to improve forecasting accuracy. Solar panels with various tilt angle combinations are set up to collect experimental data. This paper uses supervised regression learner in machine learning application for solar power prediction. In this paper, linear regression and step wise linear regression models are giving fruitful results out of 27 models that are analyzed. A detailed study of model selection is provided, alongside an examination of evaluation metrics such as Mean Absolute Error (MAE), Mean Squared Error (MSE), Root Means Square Error (RMSE), and regression coefficient R 2 scores. This study shows the effectiveness of Machine Learning (ML) in enhancing short, and medium-term solar power forecasting, supporting more efficient energy management and promoting the scalability of renewable energy systems. For the linear regression algorithm, we obtained regression coefficient (R 2 ) of 1, mean absolute percentage error (MAPE) of 0.7% and for the stepwise linear regression algorithm, we obtained a regression coefficient (R 2 ) of 1, mean absolute percentage error (MAPE) 0.45%.

关键词： Training Renewable energy sources Analytical models Machine learning algorithms Linear regression Machine learning Prediction algorithms Solar panels Forecasting Meteorology

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Quantum Hamiltonian Descent for Non-smooth Optimization

arXiv

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arXiv 2025年

作者： Leng, Jiaqi Zheng, Yufan Jia, Zhiyuan Fan, Lei Zhao, Chaoyue Peng, Yuxiang Wu, Xiaodi Simons Institute for the Theory of Computing University of California Berkeley United States Department of Mathematics University of California Berkeley United States Department of Computer Science University of Maryland College Park United States Joint Center for Quantum Information and Computer Science University of Maryland United States Department of Applied Mathematics University of Washington United States Department of Engineering Technology University of Houston United States Department of Electrical and Computer Engineering University of Houston United States Department of Industrial and Systems Engineering University of Washington United States Artephi Computing Inc United States

Non-smooth optimization models play a fundamental role in various disciplines, including engineering, science, management, and finance. However, classical algorithms for solving such models often struggle with convergence speed, scalability, and parameter tuning, particularly in high-dimensional and non-convex settings. In this paper, we explore how quantum mechanics can be leveraged to overcome these limitations. Specifically, we investigate the theoretical properties of the Quantum Hamiltonian Descent (QHD) algorithm for non-smooth optimization in both continuous and discrete time. First, we propose continuous-time variants of the general QHD algorithm and establish their global convergence and convergence rate for non-smooth convex and strongly convex problems through a novel Lyapunov function design. Furthermore, we prove the finite-time global convergence of continuous-time QHD for non-smooth non-convex problems under mild conditions (i.e., locally Lipschitz). In addition, we propose discrete-time QHD, a fully digitized implementation of QHD via operator splitting (i.e., product formula). We find that discrete-time QHD exhibits similar convergence properties even with large time steps. Finally, numerical experiments validate our theoretical findings and demonstrate the computational advantages of QHD over classical non-smooth non-convex optimization algorithms. Copyright © 2025, The Authors. All rights reserved.

关键词： Continuous time systems

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Challenges and Innovations in Multimedia and Real-Time Networking: A Review of Modeling Approaches

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Procedia computer Science 2025年 252卷 53-62页

作者： Milind Shah Kajal Parmar Priyanka Padhiyar Naina Parmar Monali Parikh Dhruvansh Patni Assistant Professor Department of Computer Engineering Sardar Vallabhbhai Patel Institute of Technology (S.V.I.T) Vasad Gujarat India Department of Computer Engineering Devang Patel Institute of Advance Technology & Research (DEPSTAR) Faculty of Technology & Engineering (FTE) Charotar University of Science & Technology (CHARUSAT) Changa 388421 India Assistant Professor Department of Information Technology Krishna School of Emerging Technology & Applied Research - Drs. Kiran & Pallavi Patel Global University (KPGU) Vadodara Gujarat India

Over the past decade, the rise of broadband and mobile Internet access has led to the widespread adoption of real-time networking and multimedia applications. These platforms have become essential for connecting individuals and supporting businesses, particularly with the growing trend of remote work. The demand for video streaming services has surged significantly and is expected to continue rising. However, this increased traffic has strained network performance, leading to congestion and diminishing the quality of service, especially during peak evening hours and the COVID-19 pandemic. Besides upgrading network infrastructure, it is crucial to develop intelligent streaming systems that adapt to network conditions and user expectations to enhance customer satisfaction. This review paper explores into the basics and introduction of Multimedia and Real-Time Networking. It also explores the advancements in Adaptive and Intelligent Networking for real-time multimedia communication. The paper formulates research questions based on the discussed topics, such as the distinctions between real-time networking and traditional networking, the ways multimedia applications adjust to fluctuating network conditions, and the challenges and limitations associated with these technologies.

关键词： Real Time Networking Multimedia Challenges Innovations Intelligent Networking

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Path Percolation in Quantum Communication Networks

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Physical Review Letters 2025年第3期134卷 030803-030803页

作者： Xiangyi Meng Bingjie Hao Balázs Ráth István A. Kovács Department of Physics Applied Physics and Astronomy Rensselaer Polytechnic Institute Troy New York 12180 USA Department of Physics and Astronomy Northwestern University Evanston Illinois 60208 USA Department of Stochastics Institute of Mathematics Budapest University of Technology and Economics H-1111 Budapest Hungary HUN-REN-BME Stochastics Research Group H-1111 Budapest Hungary Alfréd Rényi Institute of Mathematics H-1053 Budapest Hungary Northwestern Institute on Complex Systems Northwestern University Evanston Illinois 60208 USA Department of Engineering Sciences and Applied Mathematics Northwestern University Evanston Illinois 60208 USA

In a quantum communication network, links represent entanglement between qubits located at different nodes. Even if two nodes are not directly linked by shared entanglement, they can still communicate via routing protocols. However, in contrast to classical communication, each quantum communication event removes all participating links along the routed path, disrupting the quantum communication network. Here, we propose a simple model, where randomly selected pairs of nodes communicate through the shortest paths. Each time such a path is used, all participating links are eliminated, leading to a correlated percolation process that we call “path percolation.” We study path percolation both numerically and analytically and present the phase diagram of the steady states as a function of the rate at which new links are being added to the network. As a key result, the steady state is found to be independent of the initial network topologies when new links are added randomly between disconnected components. We close by discussing extensions of path percolation and link replenishment, along with their potential applications.

关键词： Quantum entanglement

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