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Mutli-Step Chain-of-Thought in Geometry Problem Solving 4

Mutli-Step Chain-of-Thought in Geometry Problem Solving

引用

4th international conference on Electronic Information engineering and computer science, EIECS 2024

作者： Wang, Long Ma, Jianhui University of Science and Technology of China Hefei China

ISBN: (纸本)9798331531409

In the realm of natural language processing, large language models (LLMs) have demonstrated superb performance in human-level reasoning and text generation, which has inspired a large number of researchers to conduct extensive research on the automatic solution of mathematical word problems. The latest work has concentrated on the construction of extensive geometry datasets for fine-tuning, leveraging huge datasets to enhance the mathematical reasoning capabilities of models. However, the majority of these work focus on calculation problem solving, and little attention is given to the more challenge proving problems. To address this gap, we introduce a multi-step chain of thought approach. By employing two-step generation approach, our method enables the model to concentrate on distinct task, thereby augmenting the model's comprehension and mathematical reasoning ability. Furthermore, we explore the application of multi-modal large language models in geometry proving problems, proposing an evaluation framework that employ the existing powerful LLMs to evaluate model's performance. © 2024 ieee.

关键词： Chains

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Research on Road Damage Detection Based on an Improved YOLOv8 Model 4

Research on Road Damage Detection Based on an Improved YOLOv...

引用

4th international conference on computer science, Electronic Information engineering and Intelligent Control Technology, CEI 2024

作者： Xiao, Yuhan Wan, Chufeng Lin, Junqing Dong, Shu Gao, Weishun Mei, Liangcai Beijing Institute of Technology Zhuhai Department of Data Science Zhuhai China

ISBN: (纸本)9798350378542

This paper presents the YOLOv8 model for road damage detection, highlighting performance enhancements. YOLOv8 features a novel backbone network, an anchor-free detection head, and new loss functions, ensuring efficient operation across various hardware platforms. A self-attention mechanism is integrated to enhance classification accuracy and robustness. Experimental comparisons indicate that while the attention-augmented YOLOv8 improves precision and recall, the modified version achieves superior overall performance. Analyses using confusion matrices and precision-recall curves demonstrate the enhanced YOLOv8's greater accuracy and stability with complex backgrounds and diverse damage types. The results confirm its superiority in practical applications, showcasing strong performance in road damage detection. © 2024 ieee.

关键词： Object detection

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Marine Propulsion Health Monitoring: Integrating Neural Networks and IoT Sensor Fusion in Predictive Maintenance 2

Marine Propulsion Health Monitoring: Integrating Neural Netw...

引用

2nd ieee international conference on Device Intelligence, Computing and Communication Technologies, DICCT 2024

作者： Anitha, K. Vijayakumar, R. Gnana Jeslin, J. Elangovan, K. Jagadeeswaran, M. Srinivasan, C. School of Computing Srm Institute of Science and Technology Department of Computing Technologies Tamil Nadu Chennai India Department of Electronics and Communication Engineering Tamil Nadu Namakkal India Rmk College of Engineering and Technology Department of Computer Science and Engineering Tamil Nadu Chennai India Dhanalakshmi Srinivasan Engineering College Department of Mechanical Engineering Tamil Nadu Chennai India Vinayaka Missison's Kirupanandha Variyar Engineering College Department of Computer Science and Engineering Tamil Nadu Salem India Saveetha University Department of Computer Science and Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Tamil Nadu Chennai India

ISBN: (纸本)9798350372847

The maritime sector is shifting towards predictive maintenance to improve marine propulsion system dependability and efficiency. This research introduces neural networks and IoT sensor fusion for marine propulsion health monitoring. Real-time operational data is collected by a sophisticated sensor array spanning crucial propulsion system components. Fusing sensor data using modern IoT algorithms gives a comprehensive overview of system health. The suggested technique uses neural networks for predictive maintenance. A deep learning model analyses sensor-fused data to detect flaws or performance deterioration. Training the neural network on past data from various operating situations allows it to adapt and forecast faults. The model's capacity to learn and develop improves its vessel operating state adaptation. Neural networks and IoT sensor fusion offer early defect identification and dynamic maintenance schedules. Low downtime, operating expenses, and marine propulsion system longevity are achieved using this strategy. Case studies and simulations indicate that the suggested system can predict and avoid significant failures, making it suitable for marine use. © 2024 ieee.

关键词： Fault detection

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Towards a Revolutionary Info-Geometric Control Theory with Potential Applications of Fokker Planck Kolmogorov(FPK) Equation to System Control, Modelling and Simulation 28

Towards a Revolutionary Info-Geometric Control Theory with P...

引用

28th international conference on automation and Computing, ICAC 2023

作者： Mageed, Ismail A Zhou, Yuyang Liu, Yefeng Zhang, Qichun University of Bradford Dept. of Computer Science BradfordBD7 1DP United Kingdom School of Computing Engineering and the Built Environment Edinburgh Napier University Edinburgh United Kingdom School of Automation and Electrical Shenyang Institute of Technology Liaoning Key Lab of Information Physical Fusion and Intelligent Cnc Machine Tool Manufacturing Fushun China

ISBN: (纸本)9798350335859

An exposition of a ground-breaking Info-Geometric approach to FPK probability density function (PDF) is undertaken in this paper. The results extend to finding the FPK Geodesic equations (GEs) of motion. Exact analytic solutions of these GES are derived. the Some potential applications of FPK to system control, modelling and simulation are provided. Challenging Open problems are given combined with conclusion and next phase research. © 2023 ieee.

关键词： Probability density function

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Minimizing Data Transmission Delay in Vehicular Networks through Network Coding-based Broadcasting 17

Minimizing Data Transmission Delay in Vehicular Networks thr...

引用

ieee Congress on Cybermatics: 17th ieee international conference on Internet of Things, iThings 2024, 20th ieee international conference on Green Computing and Communications, GreenCom 2024, 17th ieee international conference on Cyber, Physical and Social Computing, CPSCom 2024, 10th ieee international conference on Smart Data, SmartData 2024

作者： Hu, Jiali Qu, Long Song, Qinglin Zhu, Ran Faculty of Electrical Engineering and Computer Science Ningbo University Ningbo315211 China Zhejiang Faraday Laser Technology co. Ltd Wenzhou325024 China

ISBN: (纸本)9798350351637

Advances in wireless communication technology have facilitated the development of vehicular networks. Intelligent vehicles are able to realize a variety of applications and services to enhance the driving experience through Vehicle-to-Everything (V2X) communication. However, meeting the requirement of low latency in dynamic and complex vehicular environments poses a challenge. This paper presents a network coding framework based on a vehicular network data transmission model, aiming to reduce transmission latency and data dropout rate by encoding and broadcasting packets at Roadside Units (RSUs). Due to the mobility of vehicles, interference becomes more complex. To mitigate interference between data transmissions, we employ dynamic power control. However, the optimization problem involved is a mixed integer problem. To reduce the complexity, this paper introduces a Column Generation algorithm to solve the model. The algorithm decomposes the problem into a pricing problem responsible for packet scheduling and a master problem responsible for minimizing the transmission delay to find the optimal coding opportunities and broadcasting strategies for vehicle packets. Experimental results demonstrate that compared to scenarios without network coding, the proposed algorithm reduces transmission latency by 5.694% and decreases data dropout rate by 7.143%. © 2024 ieee.

关键词： Network coding

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Merging the Strengths of GPT-4 and DeepLab for Semantic Text Understanding in Digital Textbooks

Merging the Strengths of GPT-4 and DeepLab for Semantic Text...

引用

2024 international conference on Communication, computer sciences and engineering, IC3SE 2024

作者： Anusha, B. Borah, Asha Rani Mishra, Gaurav Thethi, H Pal Al-Allak, Mustafa Abdulhussein Vidhya, B. Veda Institute Of Aeronautical Engineering Department Of Computer Science And Engineering Telangana Hyderabad India New Horizon College Of Engineering Department Of Computer Science Engineering Bangalore India Amity School Of Engineering And Technology Amity University Greater Noida India Lovely Professional University Phagwara India College Of Medical Technology The Islamic University Najaf Iraq Mlr Institute Of Technology Department Of Computer Science And Engineering Telangana Hyderabad India

ISBN: (纸本)9798350366846

Gathering all the necessary data is the major objective of this study so that a comprehensive picture can be painted of how new technologies might improve students' understanding of semantic content in digital textbooks. The recommendations for this approach make use of TIFA, CEG, and ACA, or the Adaptive Complexity Adjustment. This approach combines the best features of various algorithms. TIFA makes these learning aids easy to completely comprehend by combining written and visual material. Answering students' queries and providing answers that make sense in the context is CEG's responsibility. To make learning more tailored to everyone, ACA changes the difficulty of the content depending on performance metrics. Conversely, when students ask CEG questions, it finds answers. We compare the proposed method, Semantix Blend, to two more popular approaches, Textbook Classic and Trad Edu. Interpreting text, evaluating visual information, ensuring consistency in context, enabling customisation, making content more accessible, enhancing learning results, and many more advantages are all possible with Semantix Blend. The study's findings will contribute to the improvement of digital textbooks, leading to improved instruction for students in the long term. © 2024 ieee.

关键词： Semantics

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EELMCDA: Combining evolutionary ensemble learning with matrix feature decomposition for predicting circRNA-disease associations

EELMCDA: Combining evolutionary ensemble learning with matri...

引用

2024 ieee international conference on Bioinformatics and Biomedicine, BIBM 2024

作者： Wang, Zheng Wang, Lei You, Zhu-Hong Wang, Lei Li, Yang Wang, Zhenyu Xi'an University of Technology School of Computer Science and Engineering Xi'an710048 China Northwestern Polytechnical University School of Computer Science Xi'an710048 China China University of Mining and Technology School of Computer Science and Technology Xuzhou221116 China Hefei University of Technology School of Computer Science and Information Engineering Hefei230601 China Lanzhou University of Technology School of Electrical Engineering and Information Engineering Lanzhou730050 China

ISBN: (纸本)9798350386226

Recent studies have indicated that circular RNAs (circRNAs) play a significant role in the diagnosis and treatment of disease. However, the prediction of associations between circRNAs and diseases using conventional biological methods is constrained by numerous factors. In this study, we proposed a novel computational model called EELMCDA that combines evolutionary ensemble learning (EEL) approach and matrix feature decomposition method to predict potential circRNA-disease associations. The model firstly integrates circRNA function information, disease semantic information, and circRNA and disease gaussian interaction profile kernel (GIPK) information into an integrated matrix and constructed the corresponding feature matrix, then uses the matrix feature decomposition algorithm to obtain its important feature, and finally adopted evolutionary ensemble learning module to predict circRNA-disease associations. The average accuracy of the EELMCDA model by 5-fold cross-validation on CircR2Disease, CircAtlasv2.0, Circ2Disease, and CircRNADisease datasets were 92.40%, 92.90%, 88.91%, and 90.74%, respectively. Moreover, in case studies, the 21 of the top 30 circRNA-disease pairs with the highest EELMCDA scores were validated in recent literatures. These results further demonstrate the effectiveness of EELMCDA in predicting circRNA-disease associations. © 2024 ieee.

关键词： Contrastive Learning

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Neural Networks And Machine Learning 5

Neural Networks And Machine Learning

引用

5th ieee international conference on Cybernetics, Cognition and Machine Learning Applications, ICCCMLA 2023

作者： Sayal, Anu Jha, Janhvi Chaithra, N. Gupta, Veethika Gupta, Ashulekha Gupta, Omdeep Memoria, Minakshi School of Accounting and Finance Taylor's Business School Taylor's University Malaysia Bangalore Karnataka India Doon University Department of Economics Uttarakhand Dehradun India Graphic Era Deemed to be University Department of Management Studies Uttarakhand Dehradun India School of Management Graphic Era Hill University Uttarakhand Dehradun India Uttaranchal Institute of Technology Uttaranchal University Department of Computer Science and Engineering Dehradun India

ISBN: (纸本)9798350338287

Recent years have seen an increase in the popularity of neural network (NN) research. The mammalian brain, which consists of billions of interconnected neurons, is renowned for its ability to perform computationally hard tasks, such as face recognition, body motion planning, and muscle activity control. In an effort to emulate the effectiveness of biological neural networks in learning, artificial neural networks (ANNs) were developed. The NN technique has been the topic of many studies over the last few decades, with applications in many fields including control engineering, automation, aerospace, psychology, economics, healthcare, and energy science. The objective of the discipline of machine learning is to create computers that can independently learn and improve. In this chapter, we have attempted to depict the types of neural networks and machine learning as well as their applications in different industrial disciplines such as science, commerce, and medicine. © 2023 ieee.

关键词： Neural networks

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Topology Planning of Clustered Transmission Networking for Offshore Wind Farms 8

Topology Planning of Clustered Transmission Networking for O...

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8th ieee international conference on Energy Internet, ICEI 2024

作者： Tao, Siyu Gu, Ping Feijoo-Lorenzo, Andres E. Nanjing University of Aeronautics and Astronautics College of Automation Engineering Nanjing China Nanjing University of Aeronautics and Astronautics College of Automation Engineering Center for More-Electric-Aircraft Power System Nanjing211106 China University of Vigo Department of Electrical Engineering Vigo Spain Universidade de Vigo Departamento de Enxeñería Eléctrica Vigo36310 Spain

ISBN: (纸本)9798331523558

Offshore wind energy is abundant and offers high energy density with minimal environmental impact, making it an increasingly popular field of study and development worldwide. However, offshore wind farms are confronted with challenges such as scarce resources for transmission corridors. Optimizing the topological structure of the transmission network for offshore wind farms has thus become a critical issue in their advancement. Given the presence of an onshore control center, offshore substations, and wind farm power stations, this study sets out to minimize the investment cost for the transmission network topology. A mathematical optimization model for the investment cost is developed. The paper introduces a binary particle swarm optimization (PSO) algorithm for this topological optimization. This approach is characterized by its swift convergence, minimal parameter requirements, and straightforward implementation. By refining the topological structure of offshore wind farms, the algorithm not only reduces construction costs but also decreases the need for manpower. This has significant implications for the practical application of such projects in the field. © 2024 ieee.

关键词： Offshore wind farms

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Research on improved diffusion algorithm based on gradient quantization in target localization algorithm 3

Research on improved diffusion algorithm based on gradient q...

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3rd international conference on Electronic Information engineering and computer Communication, EIECC 2023

作者： Liu, Yuanheng Yin, Yong School of Information Engineering Wuhan University of Technology Wuhan China Chongqing Research Institute Wuhan University of Technology Chongqing China

ISBN: (纸本)9798350359961

As the industrial automation field rapidly advances, the application of image processing in practical engineering becomes increasingly sophisticated. This study focuses on the target localization problem in industrial production lines, utilizing the Histogram of Oriented Gradient (HOG) algorithm to extract the gradient direction features from images. Building upon this, we propose an improved gradient diffusion algorithm. The algorithm enhances the precision and detail of each pixel's gradient direction by identifying primary gradient directions within the pixel's neighborhood and adjusting the central pixel's gradient information. This algorithm does not require a timeconsuming training phase, enhancing the robustness of image processing. It not only addresses interference from various image acquisition environments but also improves the reliability of the localization algorithm when dealing with irregularly shaped workpieces. Our experimental results have confirmed the effectiveness of the proposed method in the precise detection and localization within industrial production. When faced with various scenarios, the accuracy of its positioning exceeds 99%, and the execution time does not exceed 0.05 seconds. © 2023 ieee.

关键词： computer vision Gradient direction diffusion Gradient feature extraction HOG

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