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检索条件"任意字段=Conference on Mathematical Methods in Computer Science"

共 8109 条记录，以下是181-190 订阅

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Artificial Intelligence and its Role in Language Games in Mathematics Classrooms 3

Artificial Intelligence and its Role in Language Games in Ma...

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3rd International conference on Sentiment Analysis and Deep Learning, ICSADL 2024

作者： Shivani Choudhary, Shalu Kathuria, Samta Kumar, Gotte Ranjith Pal, Pradeepto Gupta, Manish School of Applied and Life Sciences Uttaranchal University Dehradun India Uttaranchal University LCD Division of Research and Innovation Dehradun India School of Computer Science and Artificial Intelligence SR University Warangal India Shool of Agriculture Uttaranchal University Dehradun India Lovely Professional University Punjab India

ISBN: (纸本)9798350396157

Utilizing language games in mathematics classrooms is an effective approach to engage students, promoting accessibility across diverse age groups and backgrounds. These games not only enhance comprehension of mathematical concepts but also foster problem-solving prowess and critical thinking. This article delves into the diverse categories of language games employed in math education, their specific objectives, and the advantageous impact they yield in educational settings. Moreover, it explores methods of customizing language games to cater to individual student requirements and offers guidance for educators on optimal strategies for introducing these games. The paper's ultimate goal is to present a thorough analysis of the possible benefits of language games for teaching mathematics by involving the role of artificial intelligence (AI), highlighting their potential as a valuable tool for improving mathematical understanding among students. © 2024 IEEE.

关键词： Students

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SpikingSSMs: Learning Long Sequences with Sparse and Parallel Spiking State Space Models 39

SpikingSSMs: Learning Long Sequences with Sparse and Paralle...

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39th Annual AAAI conference on Artificial Intelligence, AAAI 2025

作者： Shen, Shuaijie Wang, Chao Huang, Renzhuo Zhong, Yan Guo, Qinghai Lu, Zhichao Zhang, Jianguo Leng, Luziwei Department of Computer Science and Engineering Southern University of Science and Technology Shenzhen China ACSLab Huawei Technologies Co. Ltd. Shenzhen China School of Mathematical Sciences Peking University Beijing China Department of Computer Science City University of Hong Kong Hong Kong Pengcheng Laboratory Shenzhen China

ISBN: (纸本)157735897X

Known as low energy consumption networks, spiking neural networks (SNNs) have gained a lot of attention within the past decades. While SNNs are increasing competitive with artificial neural networks (ANNs) for vision tasks, they are rarely used for long sequence tasks, despite their intrinsic temporal dynamics. In this work, we develop spiking state space models (SpikingSSMs) for long sequence learning by leveraging on the sequence learning abilities of state space models (SSMs). Inspired by dendritic neuron structure, we hierarchically integrate neuronal dynamics with the original SSM block, meanwhile realizing sparse synaptic computation. Furthermore, to solve the conflict of event-driven neuronal dynamics with parallel computing, we propose a lightweight surrogate dynamic network which accurately predicts the after-reset membrane potential and compatible to learnable thresholds, enabling orders of acceleration in training speed compared with conventional iterative methods. On the long range arena benchmark task, SpikingSSM achieves competitive performance to state-of-the-art SSMs meanwhile realizing on average 90% of network sparsity. On language modeling, our network significantly surpasses existing spiking large language models (spikingLLMs) on the WikiText-103 dataset with only a third of the model size, demonstrating its potential as backbone architecture for low computation cost LLMs. Copyright © 2025, Association for the Advancement of Artificia Intelligence (***). All rights reserved.

关键词： State space methods

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Prediction of rolling force deviation in the hot rolling process of strip steel based on LSTM-attention and process knowledge

Prediction of rolling force deviation in the hot rolling pro...

引用

2024 International conference on computer Application and Information Security, ICCAIS 2024

作者： Wang, Pengbo Wu, Yifei Lin, Yifang School of Computer Science Beijing Information Science and Technology University Beijing100000 China Beijing University of Technology Beijing China

ISBN: (纸本)9781510689312

In the hot strip rolling process, factors such as product diversity and frequent roll changes lead to the challenge of predicting rolling force under non-steady-state working conditions. This paper proposes a hybrid artificial neural network model based on Long Short-Term Memory (LSTM) and attention mechanisms to predict the horizontal force during hot strip rolling. First, a mathematical model for rolling force prediction is established, and the key parameters influencing the rolling force are thoroughly analyzed. Then, an attention mechanism is introduced to enhance the model’s ability to capture important information from the input sequence, improving prediction accuracy. By combining LSTM and attention mechanisms, a hybrid neural network model is constructed, effectively modeling the complex nonlinear relationships in the rolling process. Experimental results show that the proposed hybrid model significantly improves prediction accuracy compared to traditional methods. The mean of the predictions is close to the actual mean, and the variance of the predictions is smaller, indicating that the model is highly stable and reliable. Additionally, the model demonstrates strong generalization ability, making it well-suited to predicting rolling force under varying working conditions. This study not only enriches the theoretical framework for modeling the hot strip rolling process but also provides a scientific basis for quality control and energy reduction in actual production. In the future, we will further optimize the model structure and explore the application of more advanced neural network techniques to promote the intelligent development of hot strip rolling technology. © The Authors.

关键词： Model predictive control

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A New MRA and AA based Algorithm for the Higher Order Reduction Uncertain Systems 9

A New MRA and AA based Algorithm for the Higher Order Reduct...

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9th International conference on science, Technology, Engineering and Mathematics, ICONSTEM 2024

作者： Shende, Vaishali Gajendra Raviraja, S. Kavitha, P. Aancy, H. Mickle Kumar, M. Jogendra Mittal, Varsha East Point College of Engineering And Technology Department of Information Science And Engineering Karnataka Bangalore560049 India University of Garden City Royal Research Foundation® Mysuru Department of Computer Studies Karnataka 570028 India R.MK. Engineerine College Department of Electrical And Electronics Eneineering Chennai Tamil Nadu Kavaraipettai601206 India Panimalar Engineering College Department of Management Studies Tamil Nadu Chennai600123 India KoneruLakshmaiah Education Foundation Department of Computer Science And Engineering Andhra Pradesh Vaddeswaram India Graphic Era Deemed To Be University Department of Computer Science And Engineering Uttarakhand Dehradun248002 India

ISBN: (纸本)9798350365092

The MRA and AA algorithms are utilized in this paper to present a novel approach for the reduction of high-order uncertain systems. The development of the program makes use of both of these mathematical approaches. As a result of this creative approach, the Continuous SISO system might be reduced. Measurement mistakes brought on by variations in the environment have the potential to render the system ambiguous, in contrast to other systems that have been reported in the literature. This strategy, in contrast to the methods that were used in the prior iteration literature, has the potential to build a stable reduced order (r-o) model from a stable initial high order uncertain system. By avoiding many of the constraints of interval arithmetic, affine arithmetic is used. One of the problems is that there is not a solution that is unbounded in many of the key scenarios. Through the utilization of typical numerical examples derived from interval literature, the methodology was validated. Among the topics that we investigate are uncertain systems, lower order models, affine arithmetic, and the modified Routh approximation approach. © 2024 IEEE.

关键词： Uncertain systems

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Feasible Robustness in Adaptive Monitoring of Complex Harmonic Time Series Using In-Parameter-Linear Nonlinear Neural Architectures 6

Feasible Robustness in Adaptive Monitoring of Complex Harmon...

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6th International conference on Industry 4.0 and Smart Manufacturing, ISM 2024

作者： Marzova, Katerina Bukovsky, Ivo Faculty of Mechanical Engineering Czech Technical University in Prague Prague Czech Republic Faculty of Science University of South Bohemia in Ceske Budejovice Ceske Budejovice Czech Republic

This work studies the meta-parameter influence on adaptive prediction of complex harmonic signals such as ball bearing vibrations and its monitoring via neural weights using in-parameter-linear nonlinear neural architecture (IPLNA). We show that Higher-Order Neural Units (HONUs) also with higher nonlinearity orders have the potential to approximate and thus monitor complex behaviour of systems with inherent linearity (such as ball bearing vibrations) without overfitting during real-time learning. We show that the presence of multicollinearities in the input state does not necessarily reduce the generalizing properties of HONU models and that the increased nonlinearity of HONUs does not necessarily introduce an overfitting problem. Therefore, due to the analogy of the mathematical architecture of HONUs to time-variant linear dynamical systems and their underlying mathematical-physical understanding, we present a basis for novel research of explainable machine learning methods for predictive maintenance. © 2024 The Authors. Published by Elsevier B.V.

关键词： Ball bearings

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A Survey of Machine Learning methods for Analyzing Synovitis Arthritis in Human Joints 19thth

A Survey of Machine Learning Methods for Analyzing Synovit...

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19th International conference on computer Aided Systems Theory, EUROCAST 2024

作者： Bąk, Artur Klempous, Ryszard Segen, Jakub Nikodem, Jan Rozenblit, Jerzy Chaczko, Zenon Kulbacki, Michal Gruszecka, Katarzyna Skoczyńska, Marta Atsushi, Ito Bożejko, Wojciech Jagielski, Dariusz Panejko, Anna Kowalczyk, Hubert Kluwak, Konrad Wojciechowski, Konrad Kulbacki, Marek Wroclaw University of Technology Wroclaw Poland Wroclaw University of Science and Technology Wroclaw Poland School of Electrical and Data Engineering University of Technology Sydney Ultimo Australia Department of Electrical and Computer Engineering University of Arizona Tucson United States Polish-Japanese Academy of Information Technology Warsaw Poland DIVE IN AI Wroclaw Poland Mathematical Institute University of Wroclaw Wroclaw Poland Faculty of Economics Chuo University Tokyo Japan Department of Rheumatology and Internal Diseases Lower Silesian Specialist Hospital Wroclaw Poland

ISBN: (纸本)9783031829598

Synovitis, characterized by inflammation of the synovial membrane in human joints, poses significant diagnostic and treatment challenges. This review presents methods and recent machine learning (ML) developments to analyze synovial arthritis in joints that help overcome these challenges. The methods described in the review include traditional ML algorithms, novel deep learning architectures and recent medical imaging techniques. Key challenges are discussed including the need for large and diverse datasets, model interpretability, generalization to different patient populations, dealing with data variability, and reducing computational complexity. The review also examines integrating multimodal data sources, advances in transfer learning, and developing robust, interpretable models as future directions. It includes enhancing early diagnostic capabilities, leveraging joint-on-a-chip simulations, and investigating signaling pathways in rheumatoid arthritis. This study aims to provide a consolidated resource for interdisciplinary researchers, clinicians and practitioners in the fields of rheumatology and medical imaging as it synthesizes current research to understand better ML methods in the detection of synovitis in human joints, paving the way for improved diagnostic and care capabilities over the patient. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Deep learning

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Applied Discrete Optimization in the Development of the Discipline "Computational methods" 10th

Applied Discrete Optimization in the Development of the ...

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10th Russian Supercomputing Days conference, RuSCDays 2024

作者： Egamov, Albert Burago, Pavel Lobachevsky University of Nizhny Novgorod Nizhny Novgorod Russia Nizhny Novgorod State Technical University Nizhny Novgorod Russia

ISBN: (纸本)9783031784613

There is a wide variety of topics of laboratory works for the teacher of the discipline "Computational methods". It is necessary that students improve their theoretical knowledge during its implementation, show their programming skills, express creative invention and ingenuity in graphical output of results, as well as find out for themselves what practical tasks they will have to face after graduation. The authors of the article propose to familiarize with the laboratory work "Development of a DSS for processing sugar beet", which meets all the above requirements. It considers a mathematical model of a real problem of the agro-industrial complex, which is closely related to discrete optimization. In addition, the scope of application of DSS systems now, in fact, includes most of the tasks assigned to IT services, therefore, the development of this DSS is a good practice for students in programming and designing such systems. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Sugar beets

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Bridging the Gap Between Hyperdimensional Computing and Kernel methods via the Nyström Method 39

Bridging the Gap Between Hyperdimensional Computing and Kern...

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39th Annual AAAI conference on Artificial Intelligence, AAAI 2025

作者： Zhao, Quanling Thomas, Anthony Hitchcock Brin, Ari Yu, Xiaofan Rosing, Tajana Department of Computer Science and Engineering UC San Diego United States

ISBN: (纸本)157735897X

Hyperdimensional computing (HDC) is an approach from the cognitive science literature for solving information processing tasks using data represented as high-dimensional random vectors. The technique has a rigorous mathematical backing, and is easy to implement in energy-efficient and highly parallel hardware like FPGAs and "processing-in-memory" architectures. The effectiveness of HDC in machine learning largely depends on how raw data is mapped to high-dimensional space. In this work, we propose NysHD, a new method for constructing this mapping that is based on the Nyström method from the literature on kernel approximation. Our approach provides a simple recipe to turn any user-defined positive-semidefinite similarity function into an equivalent mapping in HDC. There is a vast literature on the design of such functions for learning problems. Our approach provides a mechanism to import them into the HDC setting, expanding the types of problems that can be tackled using HDC. Empirical evaluation against existing HDC encoding methods shows that NysHD can achieve, on average, 11% and 17% better classification accuracy on graph and string datasets respectively. Copyright © 2025, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

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Extended Optimal Control Problem for Practical Application 4th

Extended Optimal Control Problem for Practical Application

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4th International conference on Numerical Computations: Theory and Algorithms, NUMTA 2023

作者： Diveev, Askhat Shmalko, Elizaveta Federal Research Center "Computer Science and Control" of the Russian Academy of Sciences Moscow Russia

ISBN: (纸本)9783031812408

To apply a solution of the optimal control problem directly to the control object for which model this problem was solved, it is necessary to build a system of motion stabilization along the obtained optimal trajectory. However, the construction of such a stabilization system is not provided in the classical formulation of the optimal control problem. Therefore, additional requirements for the properties of the optimal solution have been introduced into the optimal control problem. A formulation of the extended optimal control problem is introduced and the approaches to its numerical solution are discussed. One way to meet the introduced requirements is to build a stabilization motion system of an object along a program trajectory. As far as stabilization to the whole program trajectory, not from point to point on this trajectory, is rather challenging task, so for this, numerical methods of symbolic regression are proposed to be applied. The approach consists in application of machine learning by symbolic regression to the control synthesis problem. Symbolic regression allows to find a mathematical expression for control function as a function of the state space vector. Computational example of solving the extended optimal control problem for a robot group is presented. It is shown experimentally, that the solution of the extended optimal control problem is much less sensitive to disturbances, than a direct solution of the classical optimal control problem. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Stabilization

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A Model is Worth Tens of Thousands of Examples 9th

A Model is Worth Tens of Thousands of Examples

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9th International conference on Scale Space and Variational methods in computer Vision, SSVM 2023

作者： Dagès, Thomas Cohen, Laurent D. Bruckstein, Alfred M. Department of Computer Science Technion Israel Institute of Technology Haifa Israel Ceremade University Paris Dauphine PSL Research University UMR CNRS 7534 Paris75016 France

ISBN: (纸本)9783031319747

Traditional signal processing methods relying on mathematical data generation models have been cast aside in favour of deep neural networks, which require vast amounts of data. Since the theoretical sample complexity is nearly impossible to evaluate, these amounts of examples are usually estimated with crude rules of thumb. However, these rules only suggest when the networks should work, but do not relate to the traditional methods. In particular, an interesting question is: how much data is required for neural networks to be on par or outperform, if possible, the traditional model-based methods? In this work, we empirically investigate this question in two simple examples, where the data is generated according to precisely defined mathematical models, and where well-understood optimal or state-of-the-art mathematical data-agnostic solutions are known. A first problem is deconvolving one-dimensional Gaussian signals and a second one is estimating a circle’s radius and location in random grayscale images of disks. By training various networks, either naive custom designed or well-established ones, with various amounts of training data, we find that networks require tens of thousands of examples in comparison to the traditional methods, whether the networks are trained from scratch or even with transfer-learning or finetuning. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Deep neural networks

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