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

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Double Exponential Smoothing in Forecasting the Numbers of Pole-Mounted Transformer Failures 4

Double Exponential Smoothing in Forecasting the Numbers of P...

引用

4th International conference on Electrical, computer, Communications and Mechatronics Engineering, ICECCME 2024

作者： Mbuli, Nhlanhla College of Science Engineering and Technology Department of Electrical Engineering South Africa University of South Africa Roodepoort1709 South Africa

ISBN: (纸本)9798350391183

In this paper, author studied the use of double exponential smoothing (DES) method to forecast the quarterly numbers of pole-mounted transformer failures. To determine the values of smoothing constants, they formulate a nonlinear mathematical programming (NLP) optimisation problem for this purpose, propose the use of an Excel Solver approach and exhaustive search-based method, with a program written in Python, are used to solve the optimisation. The two methods are used to find the optimal parameters of the forecast and results are compared. The exhaustive search-based approach delivers superior forecast results, with the forecasted values responding better with variation in observations and a lower forecast error obtained. © 2024 IEEE.

关键词： Nonlinear programming

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Applying the Python Programming Language and Arduino Robotics Kits in the Process of Training Future Teachers of computer science 15

Applying the Python Programming Language and Arduino Robotic...

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15th International Scientific and Practical conference on Environment. Technology. Resources, ETR 2024

作者： Kyslitsyn, Vitalii Shevchenko, Lyudmila Umanets, Volodymyr Sikoraka, Lina Angelov, Yaroslav Vinnytsia Mykhailo Kotsiubynskyi State Pedagogical University Vinnytsia Ukraine Zhytomyr Medical Institute of the Zhytomyr Regional Council Zhytomyr Ukraine

ISBN: (纸本)9781713899570

The article explores the possibilities and advantages of applying the Python programming language for using Arduino robotics kits in the process of training future computer science teachers in pedagogical institutions of education. Considerable emphasis is placed on analyzing the principles of operation of simple programs and devices. This analysis visualizes the possibilities of using the Arduino platform and Python libraries to create robots that can be implemented in education and everyday life. The authors have presented a working model of a robot built and programmed on the basis of Arduino components to measure humidity in computer laboratories and develop automatic plant irrigation systems to maintain appropriate conditions in classrooms. They also provide an example of integrating the learning of the Python programming language with the use of Arduino robotics kits. This method aims to enhance the quality of training for future computer science teachers, broaden their methodological toolkit, and equip them with the ability to teach students using innovative methods. The study's results and the developed teaching materials aim to increase students' interest in STEM education and prepare a new generation of computer science teachers for the challenges of the modern technological world. This will expand their methodological arsenal and develop their ability to use integrated technical, engineering, and mathematical solutions to solve theoretical and practical problems. This study can serve as a guide for popularizing the use of Arduino and Python in educational institutions. It may encourage future computer science teachers to introduce STEM and practical teaching methods, which can contribute to better student learning and improve the quality of professional education in the field of information technology. The study presents opportunities for utilizing modern digital technologies in vocational education and encourages interest in STEM education among computer

关键词： Python

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Analysis of Sensor Signals with Machine and Deep Learning Algorithms 11

Analysis of Sensor Signals with Machine and Deep Learning Al...

引用

11th International Scientific conference on computer science, COMSCI 2023

作者： Yotov, Ognyan Borislavov Aleksieva-Petrova, Adelina Plamenova Technical University of Sofia German Engineering and Industrial Management Faculty 8 Kliment Ohridski blvd Sofia1000 Bulgaria Technical University of Sofia Faculty of Computer Systems and Technologies 8 Kliment Ohridski blvd Sofia1000 Bulgaria

ISBN: (纸本)9798350325256

Industry 4.0 relies heavily on data generation and analysis. Sensor signals are difficult for analysis using traditional methods and mathematical techniques. Machine and Deep Learning algorithms in combination with mathematical transformations offer effective new ways of approaching the difficult problems of processing sensor signals. The paper's goal is to propose and validate the data-driven prediction model to analyze sensor data based on machine and deep learning algorithms. The models show promising results in predicting wear values and open the problem to further research. © 2023 IEEE.

关键词： Learning algorithms

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Bilevel Optimization-Based Decomposition for Solving Single and Multiobjective Optimization Problems 13th

Bilevel Optimization-Based Decomposition for Solving Single...

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13th International conference on Evolutionary Multi-Criterion Optimization, EMO 2025

作者： Sinha, Ankur Pujara, Dhaval Singh, Hemant Kumar Brij Disa Centre for Data Science and Artificial Intelligence Indian Institute of Management Gujarat Ahmedabad380015 India School of Engineering and Technology University of New South Wales CanberraACT2610 Australia

ISBN: (纸本)9789819635054

Real world optimization problems contain multiple complexities that are often not tractable if one completely relies on mathematical programming or metaheuristic approaches, as each approach has unique strengths and limitations in dealing with the complexities in optimization problems. For instance, metaheuristic methods have an edge in tackling non-regularities like discontinuity, non-convexity, and discreteness, given that problem has few decision variables (low dimensionality). Contrary to that, mathematical programming based classical methods can efficiently solve high dimensional problems if the problem encompasses regular functions as constraints and objective function. Thus, non-regular and high-dimensional optimization problems pose challenges to both approaches for getting solved using the method explicitly from any one approach. To excel in this situation, this study proposes a difficulties separation approach that enables to convert a single level optimization problem into bilevel optimization problem and solve the problem synergistically by applying methods from both complementary approaches. We demonstrate the benefits of proposed bilevel based decomposition approach on a wide range of single and multiobjective test problems. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Optimization algorithms

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Spread Spectrum Steganography in Audio Containers 5

Spread Spectrum Steganography in Audio Containers

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5th International conference on Control Systems, mathematical Modeling, Automation and Energy Efficiency, SUMMA 2023

作者： Zhelezov, Stanimir Kordov, Krasimir Nachev, Atanas Pavlova, Daniela Konstantin Preslavsky University of Shumen Dept. Computer Science and Technologies Shumen Bulgaria Konstantin Preslavsky University of Shumen Dept. Computer Informatics Shumen Bulgaria University of Library Studies and Information Technologies Sofia Bulgaria

ISBN: (纸本)9798350309126

In this paper, attention is paid to audio containers and the possibilities of using them as carriers of steganographic information. One of the most proven methods for increasing the reliability of stegoalgorithms is distributed embedding. An embedding method based on the use of a pseudo-random sequence generator and an algorithm for its implementation are proposed. The proposed method is investigated and the results presented show that it meets standard reliability metrics and is fully suitable for use in real-world settings. © 2023 IEEE.

关键词： Containers

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Massive Parallel Simulation for Tensor Network Contraction Ordering Solving 4

Massive Parallel Simulation for Tensor Network Contraction O...

引用

4th International conference on Neural Networks, Information and Communication Engineering, NNICE 2024

作者： Xu, Wei Tong, Weiqin Liu, Xiaoyang Shanghai University School of Computer Engineering and Science Shanghai China Rensselaer Polytechnic Institute Department of Computer Science New York United States

ISBN: (纸本)9798350394375

Tensor network contraction is a powerful mathematical tool for dealing with complex systems and high-dimensional data, which is widely used in classical simulations of molecules and quantum circuits. To improve the performance of simulations, existing methods are implemented by searching the optimal contraction orderings for tensor networks. In particular, some reinforcement learning (RL) methods show a greater potential for solving. However, with the increasing scale of simulation, the number of tensors in the corresponding tensor network is huge, the existing RL methods suffer from sampling bottlenecks and slow solving speed, which make it difficult to efficiently solve for high-quality contraction orderings. In this paper, we design a massive parallel simulation environment for tensor network contraction ordering solving on a single GPU. We experimented with multiple numbers of parallel environments on the tensor-train network, and achieved the highest speedup when the number of environments was 26. We also have experimented on large-scale tensor-train networks and tree tensor networks, and compared to Kahypar and RL-Baseline, our method achieves average speedup of 8.96× and 7.48× on tensor-train networks, and 10.03× and 7.48× on tree tensor networks. © 2024 IEEE.

关键词： Tensors

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A Robust Deep Feature Fusion Model for Skin Cancer Classification 3rd

A Robust Deep Feature Fusion Model for Skin Cancer Classific...

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3rd International conference on Advanced Computing and Applications, ICACA 2024

作者： Mandal, Diptarka Ray, Amartya Sarkar, Sujan Ojjela, Odelu Sarkar, Ram School of Computer Engineering and Mathematical Sciences Defence Institute of Advanced Technology Maharashtra Pune411025 India Department of Computer Science and Engineering Jadavpur University West Bengal Kolkata700032 India

ISBN: (纸本)9789819747986

Skin cancer ranks among the deadliest forms of cancer worldwide, leading to numerous fatalities annually. Hence, the development of efficient computer-aided diagnosis (CAD) systems has become a pressing need to help medical professionals classify various types of skin cancer. This paper introduces a novel method which combines the outcomes of two deep learning models, leveraging the feature fusion strategy. Our approach utilizes Xception and Google’s Big Transfer (BiT-M) model as base learners, complemented by a squeeze and excitation attention module to enhance the fused feature maps. We also establish a combined loss function to optimize our model. We have evaluated the model on the challenging ISIC 2017 dataset and obtained an impressive accuracy of 0.795, surpassing the performance of many recently published methods within the literature. The source code of the proposed model is available at https://***/Cmatermedicalimageanalysis/ISIC2017. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Lung cancer

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SCMamba: A Space Correction State Space Model for Image Restoration 7

SCMamba: A Space Correction State Space Model for Image Rest...

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7th International conference on computer Information science and Application Technology, CISAT 2024

作者： Yang, Lei Liu, Jingyi Shi, Ze Shi, Caijuan Capital Normal University College of Fine Arts Beijing China Capital Normal University School of Mathematical Sciences Beijing China Wuhan Technology and Business University College of Computing and Automation Wuhan China North China University of Science and Technology College of Artificial Intelligence Tangshan China

ISBN: (纸本)9798350375107

Recent advancements in image restoration (IR) have shown that CNNs and Transformers yield impressive results. However, existing IR methods often suffer from limited receptive fields, high computational complexity, leading to insufficient global representation of the restored images and unacceptable computational costs. Inspired by linear complexity and global receptive field of the State Space Model (SSM), we integrate Mamba to IR tasks for learning long-term dependencies with global receptive fields at a linear computational cost. We propose the Space Correction Mamba (SCMamba) framework, which leverages a global receptive field for long-term pixel context modeling to restore more image details. Experiments on the public datasets Set5, Set14 and Urban100 show that SCMamba achieves highly competitive results. © 2024 IEEE.

关键词： Restoration

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An Evaluation System for Large Language Models based on Open-Ended Questions 11

An Evaluation System for Large Language Models based on Open...

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11th IEEE International conference on Cyber Security and Cloud Computing, CSCloud 2024

作者： Cao, Zhiyuan Ma, Zeyu Chen, Mingang Computer and Information Engineering College Shanghai Polytechnic University Shanghai Key Laboratory of Computer Software Testing and Evaluating Shanghai Development Center of Computer Software Technology Shanghai China Shanghai Key Laboratory of Computer Software Testing and Evaluating Shanghai Development Center of Computer software Technology Mathematical and Science College Shanghai Normal University Shanghai China Shanghai Key Laboratory of Computer Software Testing and Evaluating Shanghai Development Center of Computer software Technology Shanghai China

ISBN: (纸本)9798350376982

We designed a large language model evaluation system based on open-ended questions. The system accomplished multidimensional evaluation of LLMs using open-ended questions, and it presented evaluation results with evaluation reports. Currently, the evaluation of large-scale language models often exists with two prominent limitations: (1) The evaluation methods are often single-minded, resulting in less credible results. (2) Most evaluations are based on datasets with closed-ended questions, treating generative large language models as discriminative models, which fails to adequately reflect the high output flexibility characteristic of these models. For these two limitations, we proposed an evaluation system for LLMs based on open-ended questions. Our experiments on the adapted open-source datasets demonstrated the effectiveness of this system. © 2024 IEEE.

关键词： Natural language processing systems

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Algoviz—Real-Time Insight of Algorithms 3rd

Algoviz—Real-Time Insight of Algorithms

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3rd International conference on Emerging Trends in Expert Applications and Security, ICE-TEAS 2024

作者： Prasad, S. Ashwini Hanumanthappa, M. Supriyaa, K. Rathore, Vijay Singh Department of Computer Science and Applications Bangalore India Department of Computer Science Engineering Jaipur India

ISBN: (纸本)9789819737444

Algorithms are central objects of every nontrivial computer application but their analysis and design are a great challenge. While traditional methods involve mathematical and empirical approaches, there exists a third avenue for studying algorithms, i.e. the algorithm visualizer. Algorithm visualizer is an interactive software tool designed to enhance the understanding and learning of various algorithms through visual representation. This tool provides a dynamic and engaging way to explore complex computational processes, making them more accessible to both novice learners and experienced programmers. The visualization platform offers a diverse range of algorithms, spanning data structures, sorting methods, graph traversal techniques, path finding algorithms, and more. Through real-time interaction, users gain insights into algorithmic operations, fostering a deeper understanding of their logic and behaviour. Users can customize inputs, experiment with different scenarios, and observe, how algorithmic choice impacts outcomes in a visual and interactive manner. The primary goal of algorithm visualizer is to elucidate the inner workings of various known algorithms by presenting them graphically, thereby simplifying their complexities for comprehension. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： computer debugging

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