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检索条件"任意字段=14th International Conference on Algorithmic Learning Theory"

共 832 条记录，以下是51-60 订阅

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On the Complexity of learning Programs 19th

On the Complexity of Learning Programs

引用

19th international conference on Unity of Logic and Computation (CiE)

作者： Brattka, Vasco Univ Bundeswehr Munchen Fac Comp Sci Neubiberg Germany Univ Cape Town Dept Math & Appl Math Rondebosch South Africa

ISBN: (纸本)9783031369773;9783031369780

Given a computable sequence of natural numbers, it is a natural task to find a Godel number of a program that generates this sequence. It is easy to see that this problem is neither continuous nor computable. In algorithmic learning theory this problem is well studied from several perspectives and one question studied there is for which sequences this problem is at least learnable in the limit. Here we study the problem on all computable sequences and we classify the Weihrauch complexity of it. For this purpose we can, among other methods, utilize the amalgamation technique known from learning theory. As a benchmark for the classification we use closed and compact choice problems and their jumps on natural numbers, which correspond to induction and boundedness principles, as they are known from the Kirby-Paris hierarchy in reverse mathematics. We provide a topological as well as a computability-theoretic classification, which reveal some significant differences.

关键词： Artificial intelligence

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Integrating machine learning and IoT into apiary management to optimize bee health and production 19

Integrating machine learning and IoT into apiary management ...

引用

19th international conference on Future Networks and Communications, FNC 2024 / 21st international conference on Mobile Systems and Pervasive Computing, MobiSPC 2024 / 14th international conference on Sustainable Energy Information Technology, SEIT 2024

作者： Vatskel, Vladimir Biloshchytskyi, Andrii Neftissov, Alexandr Kuchanskyi, Oleksandr Andrashko, Yurii Biloshchytska, Svitlana Sachenko, Illia Astana IT University Astana010000 Kazakhstan Department of Information Technologies Kyiv National University of Construction and Architecture Kiev01001 Ukraine Department of Information Systems and Technology Taras Shevchenko National University of Kyiv Kyiv01001 Ukraine Department of System Analysis and Optimization Theory Uzhhorod National University Uzhhorod88000 Ukraine

this article discusses integrating machine learning and IoT technologies into bee apiary management systems. the research aims to optimize bee conditions, improve their health and increase honey production. Using machine learning, large amounts of data collected by hive sensors are analyzed to predict and prevent various bee health problems effectively. Particular attention is paid to methods of controlling temperature in hives through pulse-width modulation technology, which provides accurate and energy-efficient regulation of temperature conditions. A method for predicting temperature in hives for effective apiary management is also proposed. © 2024 the Authors. Published by Elsevier B.V.

关键词： beekeeping energy efficiency Internet of things machine learning pulse width modulation sustainable agriculture temperature control

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Design and Implementation of English Intelligent learning System from the Perspective of Distributed Cognition 14

Design and Implementation of English Intelligent Learning Sy...

引用

14th international conference on Measuring Technology and Mechatronics Automation, ICMTMA 2022

作者： Liu, Shan Jiang, Guoxin Zhejiang Gongshang University Hangzhou College of Commerce Hangzhou310000 China Zhejiang Gongshang University Hangzhou310018 China

ISBN: (纸本)9781665499781

From the perspective of distributed theory, there is no effective learning method combining mobile communication technology with digital learning. therefore, this paper designs an intelligent college English learning system based on mobile platform. Based on the analysis of the theory of distributed cognitive learning and the basic model of mobile learning, the physical framework of college English mobile learning is provided. then the design is based on eclipse IDE, the server is based on J2EE technology and MVC mode, and the client adopts Android SDK. According to learners' knowledge level and Ebbinghaus forgetting curve rule, the intelligent recommendation module can push learners' learning content personalized. the final running test results of the system prove the feasibility of the system, and it can provide the mobile learning function with stable running, perfect function and friendly interaction. © 2022 IEEE

关键词： learning systems

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From Data to Model: Evolutionary learning with Generative Neural Systems 1

引用

14th international conference on Soft Computing and Pattern Recognition, SoCPaR 2022, and the 14th World Congress on Nature and Biologically Inspired Computing, NaBIC 2022

作者： Dolgikh, Serge Department of Information Technology National Aviation University Kyiv Ukraine

ISBN: (数字)9783031275241

ISBN: (纸本)9783031275234

A successful strategy of learning systems, whether artificial or biological, is construction of consistent conceptual models of sensory environments. An essential question in both the theory and practical engineering of learning systems is how successful learning with data of increasing complexity can be achieved without massive prior domain knowledge. In this work the question was approached by application of generative neural learning systems of generic, incrementally evolvable architecture with sets of visual data of increasing complexity. It was demonstrated that successful generative learning with visual data, from simple geometric shapes to realistic images such as handwritten digits was possible by incremental change of a small number of architectural parameters, such as the size of the latent layer and sparsity constraint, with an effective two-dimensional learning space. Generalizing the results obtained with evolvable neural generative architectures, an approach of evolutionary learning was proposed pointing a direction of research that could connect sensory data with successful learning models, and offering insights into exceptional effectiveness of neural networks in modeling complex sensory environments. © 2023, the Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Machine learning

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Design and Research of Circuit Experiment MOOC based on Blended learning 23

Design and Research of Circuit Experiment MOOC based on Blen...

引用

14th international conference on E-Education, E-Business, E-Management and E-learning, IC4E 2023

作者： Yi, Liu Xiangjun, Wang College of Electrical Engineering Naval Univ. of Engineering Hubei Wuhan China

ISBN: (纸本)9798400700651

the university circuit experiment course is an auxiliary course of the university circuit course, which is mainly used for the verification of the theory and the cultivation of students' practical ability. Aiming at the problems in the course of circuit experiment training, the design idea of MOOCS based on blended learning is put forward. this paper discusses the teaching objectives, teaching strategies, teaching evaluation and other aspects in the design process. this paper focuses on the teaching strategies and methods to improve the teaching effect, from the aspects of adjusting the teaching system, choosing the teaching content reasonably and designing the teaching process scientifically. © 2023 ACM.

关键词： Design

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Research on the Construction of Community Education and learning Community in the Era of Big Data 2

Research on the Construction of Community Education and Lear...

引用

2nd international conference on Digitalization and Management Innovation: Build a New Ecology of Lifelong Education, DMI 2023

作者： Chen, Xuan School of Foreign Languages Xiangnan University Chenzhou China

ISBN: (纸本)9781643684529

In the era of big data, the internal structure of the community education and learning community should be balanced and open, and the education management should be standardized and orderly. However, in reality, there are many problems in the construction process of community education and learning communities, such as a simple structure of learning communities, a single and scarce environment, and arbitrary educational management. Nowadays, community education construction should focus on promoting the change of internal mechanism, exploring community learning content, constructing the new O2O learning forms and enhancing the learning motivation of core members. Only by building a good environment for the entire learning community, optimize resource allocation and build a good ecology, can we promote the construction and development of community education and learning communities. © 2023 IOS Press. All rights reserved.

关键词： Big data

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learning Magnitudes of Energy Consumption with Symbolic or Iconic Representations 14th

Learning Magnitudes of Energy Consumption with Symbolic or I...

引用

14th international conference on the theory and Application of Diagrams (Diagrams)

作者： de Vries, Erica Schwartz, Neil Galilee, Martin Univ Grenoble Alpes LaRAC F-38000 Grenoble France Calif State Univ Chico Chico CA 95929 USA

ISBN: (纸本)9783031712906;9783031712913

learning about the magnitude of energy consumption is crucial for responsible energy management in today's society. this study investigates the use of symbolic and iconic representations for learning and mentally comparing the energy consumption of (sets of) appliances. We conducted an experiment in which participants learned about the magnitude of energy consumption either with symbolic representations (Arabic numerals) or with iconic representations (bars of different heights). Participants then performed a recall and a mental comparison task varying in the number of appliances involved and in the distance in energy consumption on both sides. the findings indicate that both types of representations allow learning magnitudes. Moreover, the results showed a distance effect indicating that mental comparisons are conducted with analog internal representations. Finally, compared to symbolic representations, mental comparisons of magnitudes learned with iconic representations, despite being slightly less accurate, were much faster. these results suggest that iconic representations can be an effective tool in environmental education and communication.

关键词： Symbolic and iconic representations mental comparisons magnitudes learning

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Toward Model-Assisted Safe Reinforcement learning for Data Center Cooling Control: A Lyapunov-based Approach 23

Toward Model-Assisted Safe Reinforcement Learning for Data C...

引用

14th ACM international conference on Future Energy Systems (e-Energy)

作者： Cao, Zhiwei Wang, Ruihang Zhou, Xin Wen, Yonggang Nanyang Technol Univ Singapore Singapore Jiangxi Sci & Technol Normal Univ Nanchang Jiangxi Peoples R China

ISBN: (纸本)9798400700323

this paper considers intelligent data center cooling control via the Deep Reinforcement learning (DRL) approach to improve data center sustainability. Existing DRL-based controllers are trained with a simplified data hall thermodynamic model which assumes uniform room temperature distribution. this assumption is not valid for a real-world data center with highly nonuniform temperature distribution. Furthermore, most of them cannot guarantee thermal safety during the DRL learning process. To bridge these gaps, we propose LyaSafe, a model-assisted safe DRL approach for data center cooling control. To address the safety evaluation issue, we develop a coupled model that combines a differentiable surrogate data hall thermodynamics model with the energy model. It can simulate both data hall temperature distribution and the facility energy consumption. To address safe learning, we introduce a novel constrained Markov Decision Process (CMDP) formulation for data center cooling control by considering the Rack Cooling Index (RCI), the best-practice metric for evaluating compliance with ASHRAE data center thermal guidelines. the objective is to minimize data center carbon footprints while regulating the RCI within a threshold. We first derive the safety set based on the concept of the virtual queue and Lyapunov stability theory. Next, we rectify unsafe actions from the DRL agent by projecting them to the safety set. We evaluate LyaSafe in a data center hosting 20 racks and 299 servers. Evaluation results show that LyaSafe can ensure strict safety during the DRL learning while achieving up to 50 metric tons of annual carbon emission savings using Singapore's statistics. Moreover, we conduct root cause analysis for the savings, revealing the importance of joint control of the data hall and the chiller plant.

关键词： Data center cooling control safe reinforcement learning Lyapunov stability theory

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Experiments on the Generalization of Machine learning Algorithms 1

引用

14th international conference on Artificial General Intelligence (AGI)

作者： Franz, Arthur Odessa Ukraine

the inductive programming system WILLIAM is applied to machine learning tasks, in particular, centralization, outlier detection, linear regression, linear classification and decision tree classification. these example... 详细信息

ISBN: (数字)9783030937584

ISBN: (纸本)9783030937577;9783030937584

关键词： Inductive programming Incremental compression algorithmic complexity Machine learning WILLIAM Generalization

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Analysis of Joint Failure Pattern of Reinforced Concrete Special-Shaped Column Based on Neural Network and Interpretable Machine learning 14th

Analysis of Joint Failure Pattern of Reinforced Concrete Spe...

引用

14th international conference on Frontier Computing, FC 2024

作者： Ji, Yongjie Bai, Yuehua China United Engineering Co. Ltd. Hangzhou310052 China School of Management Wuhan Donghu University Hubei Wuhan430212 China

ISBN: (纸本)9789819627974

the purpose of this study is to make use of machine learning theory to deeply study the mechanical performance of steel reinforced concrete special-shaped column joints by data driven instead of the traditional mechanism driven method. In this study, the node data set of reinforced concrete special-shaped column containing several key parameters was established, and the improved neural network algorithm was used to construct the prediction model, and a high precision of the bearing capacity prediction was obtained. through the combination of experimental data and computer vision model, the formation process and failure form of joint cracks are analyzed, and an image processing method without human intervention is proposed, which can effectively reduce the error. Based on explainable machine learning theory, we explain and analyze the factors that affect the bearing capacity of special-shaped column joints, and reveal the role of key factors in the bearing capacity of joints. this research combines neural network, computer vision and interpretable machine learning to provide comprehensive technical support and in-depth explanation for seismic design of reinforced concrete special-shaped column joints. © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Seismic design

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