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Enhancing programming Learning with LLMs: Prompt engineering and Flipped Interaction 4

Enhancing Programming Learning with LLMs: Prompt Engineering...

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4th Asia Service sciences and Software engineering conference, ASSE 2023

作者： Cowan, Brendan Watanobe, Yutaka Shirafuji, Atsushi Graduate Department of Computer and Information Systems University of Aizu Aizu-Wakamatsu Japan Department of Computer Science and Engineering University of Aizu Aizu-Wakamatsu Japan

ISBN: (纸本)9798400708534

Due to their robustness, large language models (LLMs) are being utilized in many fields of study, including programming and education. Notably, they can be used by programmers by interfacing with their IDEs to assist with development, and in education by giving students meaningful and immediate feedback. In this paper, we propose and explore the groundwork of a framework designed to combine these two applications of LLMs. the framework acts as a facilitator between the LLM and the student by reading the student's prompts before filtering and modifying them and sending them to the LLM. the intent is that this will improve the responses from the LLM, thereby improving the student's learning experience. We discuss the framework in detail and analyze the value of individual responses returned from the LLM as a result of our framework. We conclude that the framework causes the LLM to give helpful responses in comparison to how it would respond without the framework. © 2023 Owner/Author.

关键词： Students

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Creating New programming Experiences Inspired by Boxer to Develop Computationally Literate Society 22

Creating New Programming Experiences Inspired by Boxer to De...

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6th international conference on the art, science, and engineering of programming (programming)

作者： Guzdial, Mark Univ Michigan Comp Sci & Engn Div Ann Arbor MI 48109 USA

ISBN: (纸本)9781450396561

Boxer suggests a medium that might be used by a computationally literate society. We do not have a computationally literate society, but we do have one where computation is pervasive. Reaching computational literacy will require us to invent media that reach citizens where they are, so that they can use computation for the kinds of tasks envisioned for Boxer. Teaspoon languages are task-specific languages designed to be highly usable. By developing and disseminating Teaspoon languages, we might move towards creating a computationally literate society.

关键词： computational literacy end-user programming Teaspoon languages

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A Multi-scale Residual Network Based on the Multi-head Attention Mechanism for Motor Imagery EEG Decoding 4

A Multi-scale Residual Network Based on the Multi-head Atten...

引用

4th international conference on Industrial Automation, Robotics and Control engineering, IARCE 2024

作者： Li, Ketong Liu, Xiaodong Chen, Qian Chen, Peng School of Mechanical Engineering Southwest Jiaotong University Chengdu China

ISBN: (纸本)9798350380323

Recent advancements in deep learning have led to increased application in brain-computer interfaces (BCIs) for decoding motor imagery electroencephalogram (EEG) signals. Nevertheless, current methods still present considerable challenges in effectively decoding these signals. Convolutional neural networks (CNNs), most used in this domain, are constrained by limitations associated with their kernel size and inherently limited receptive fields. this restricts their capacity to fully capture the global spatial and temporal dependencies present in EEG signals, which is crucial for accurate decoding. In response to these limitations, we propose a novel approach: a multi-scale residual network based on the multi-head attention mechanism. this model is designed to overcome the constraints of traditional CNNs by incorporating a multi-head attention layer, four convolutional layers, and a pooling layer, which enable the extraction of global and local features. To verify the effectiveness of our model, we conducted an experiment involving left- and right-hand fist imagery. We evaluate the performance of our model against several state-of-the-art models currently used in the field. the experimental results indicate that our model performs significantly better than the baseline model, with superior decoding accuracy, and our model is faster and more stable in the convergence of the loss function. It achieves an optimal balance between model complexity and accuracy, which makes it more advantageous in practical applications. © 2024 IEEE.

关键词： Brain computer interface

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Design of appearance patent retrieval system based on MapReduce cluster framework 4

Design of appearance patent retrieval system based on MapRed...

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4th international conference on Telecommunications, Optics, and Computer science, TOCS 2023

作者： Ni, Weimin Sun, Huifen Finance and Economics Art Department Zhenjiang Vocational Technical College Zhenjiang China Department of Information Engineering Zhenjiang Branch of Jiangsu Union Technical Institute Zhenjiang China

ISBN: (数字)9781510679849

ISBN: (纸本)9781510679832

the MapReduce task scheduling mechanism and the HDFS storage strategy in Hadoop are lower in the performance of complex heterogeneous application scenarios, and bave become the botteneck of mass data processing. the addresses based on Hadoop platform design patent image retrieval system which is a concrete, in default of MapReduce task scheduling mechanism and HDFS storage strategy in practical application scene performance is poor, respectively, are proposed optimization measures, improve the performance of the system. the main work is as follows: (1) the design principle and execution flow of Hadoop framework, HDFS and MapReduce are deeply analyzed. (2) Aiming at the problem of the actual use of the Hadoop platform in the actual use of the image patent retrieval system, the improvement of the LASE task scheduling strategy and the HIFI storage strategy are improved respectively. (3) the optimization strategy of the image retrieval for the Hadoop platform is verified by experiments. the experimental results show that the optimized system can improve the performance of the Hadoop platform, and reduce the response time of the users’ request. © 2024 SPIE.

关键词： MapReduce

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Rethinking the expressiveness of small objects in UAV scenes via adaptive multi-scale and context-aware feature fusion 4

Rethinking the expressiveness of small objects in UAV scenes...

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4th international conference on Machine Learning and Computer Application, ICMLCA 2023

作者： Wang, Xinghua Liu, Weilin Wang, Lunqian Zhang, Zekai Zhang, Jinglin Wang, Xing The School of Information Science and Engineering Linyi University Linyi China The School of Internet of Things Qilu University of Technology Jinan China The School of Control Science and Engineering Shandong University Jinan China

ISBN: (数字)9781510680265

ISBN: (纸本)9781510680258

Compared to conventional observations, Unmanned Aerial Vehicle (UAV) observations often result in a large number of small objects that are fuzzy and lack sufficient feature detail. In this paper, a Dynamic Meta Tandem Transformer (DMTT) network is proposed to solve the small objects problem under the view of the UAV field. We design an Adaptive Cross-dimensional Attention (ACA) to dynamically focus on important feature information in multiple dimensions. a Tandem Pooling Layer (TPL) to reduce the number of parameters and maintain high accuracy. Based on ACA and TPL, Attention Meta Feature Fusion (AMFF) and Adaptive Tandem Transformer (ATT) are proposed. AMMF can capture fine-grained spatial information to address the lack of information on small objects. ATT can aggregate global contextual information to enhance the position of small objects. Extensive experiments on the Visdrone dataset, DMTT improves by 1.3% on the AP metrics compared to the state-of-the-art model. On the UAVDT dataset, DMTT improves by 2.3% on the AP metrics compared to the state-of-the-art model. Meanwhile, the FPS metrics of DMTT on Visdrone and UAVDT reach 30.1 and 30.5, respectively. the results show that DMTT has both accuracy and speed. Moreover, DMTT improves by 0.3% (AP 46.9%) on the MS-COCO dataset, which is encouraging and competitive. © 2024 SPIE.

关键词： Antennas

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Universal Adversarial Attack for Trustworthy LiDAR based Object Detection in Embedded Applications 4

Universal Adversarial Attack for Trustworthy LiDAR based Obj...

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4th international conference on Intelligent Technology and Embedded Systems, ICITES 2024

作者： Cai, Mumuxin Wang, Xupeng Sohel, Ferdous Lei, Hang School of Information and Software Engineering University of Electronic Science and Technology of China Chengdu China School of Information Technology Murdoch University Murdoch Australia

ISBN: (纸本)9798331506292

3D object detection based on deep neural networks (DNNs) has widely been adopted in the field of embedded applications, such as autonomous driving. Nonetheless, recent studies have demonstrated that LiDAR data tends to exhibit intense corruptions, resulting in the failure of 3D object detection tasks. Considering the vulnerability of existing DNNs and the wide applications of 3D object detection to safety-critical scenarios, the robustness of deep 3D detection models under adversarial attacks is investigated in this work. the proposed universal adversarial attack is encoded into a perturbation voxel, which adds point-wise perturbations to benign LiDAR scenes. the detector-level perturbation voxel is generated by suppressing the detector's predictions on training samples, which covers the entire perceptual range of the detector. the designed perturbation voxels can be applied to the entire scene to simulate the global perturbation inherent in LiDAR. And it can adapt to other detectors with various point cloud representations, which makes attack universal. To test the effectiveness of the proposed attack, it was launched against a number of deep 3D detectors using several datasets. the results demonstrate its superiority over existing art. © 2024 IEEE.

关键词： 3D object detection LiDAR sensors Point cloud Security of embedded applications Universal adversarial attack

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Reward Design Framework Based on Reward Components and Large Language Models 4

Reward Design Framework Based on Reward Components and Large...

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4th international conference on artificial Intelligence, Robotics, and Communication, ICAIRC 2024

作者： Jin, Kexin Tian, Guohui Huang, Bin Cui, Yongcheng Zheng, Xiaoyu Shandong University School of Control Science and Engineering Jinan China

ISBN: (纸本)9798331531225

the application of Large Language Models (LLMs) in the field of robotics has gained widespread attention. Due to their powerful code generation and contextual understanding capabilities, these models can generate reward functions from task commands, prompts, and environmental code, thus aiding robots in acquiring skills through reinforcement learning (RL). However, reward functions generated by existing models often suffer from issues of low executability and success rates, particularly when handling vague or complex task commands. these models usually require multiple iterations of optimization to achieve the desired outcomes. To address this challenge, we propose a reward function generation method based on reward components, which leverages human-level prior knowledge to improve generation efficiency and accuracy. Specifically, we have constructed a task→reward components dataset and fine-tuned a Reward Component Generator (RCG) using this dataset. the RCG then guides the automatic generation of reward functions for reinforcement learning tasks. Experimental results demonstrate that our method significantly improves reward executability, accuracy, and task success rate compared to state-of-the-art approaches. For more information, please visit our project website at: https://***/RCG/ © 2024 IEEE.

关键词： Reinforcement learning

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An Advanced Deep Learning Approach for Dietary Recommendations using ROBERTA 4

An Advanced Deep Learning Approach for Dietary Recommendatio...

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4th international conference on Intelligent Technologies, CONIT 2024

作者： Rani, Bajjuri Usha Joshnavalli, Terli Reddy, Betha Srikanth Sreelaasya, Atluri Department of Computer Science and Engineering Mylavaram India

ISBN: (纸本)9798350349900

this project introduces a novel Food Recommendation System empowered by the Roberta model, a state-of-the-art transformer-based architecture in natural language processing. Leveraging Roberta's advanced language understanding capabilities, our system aims to revolutionize the domain of dietary recommendations by providing personalized and context-aware suggestions to users. the Roberta model plays a pivotal role in capturing intricate textual nuances related to nutritional content, dietary preferences, and individual health profiles, thereby enhancing the accuracy and relevance of the recommendations. the methodology involves the integration of Roberta into the recommendation system, detailing the fine-tuning process and adaptation of the model to the unique challenges posed by dietary recommendation tasks. We explore the incorporation of relevant nutritional databases, ensuring that the Roberta model is well-versed in the domain-specific knowledge required for effective food suggestions. the system's performance is evaluated through various metrics, showcasing its ability to outperform traditional rule-based and machine learning-based approaches in providing tailored dietary advice. Furthermore, this project presents insightful curves and analyses derived from the model's training process, illustrating the learning trajectory, and highlighting key milestones in its proficiency. the experimental results demonstrate the system's effectiveness in adapting to diverse user preferences and evolving dietary trends, establishing its potential to positively impact users' health and well-being. Ultimately, our Food Recommendation System, driven by the Roberta model, signifies a significant advancement in the fusion of deep learning and nutritional science. the methodology curves presented herein offer a comprehensive understanding of the model's learning dynamics, emphasizing its role in revolutionizing the landscape of personalized dietary recommendations. the promising res

关键词： Deep reinforcement learning

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User Linguistic Style Awareness and Interest-Driven Conversational Recommender Systems 4

User Linguistic Style Awareness and Interest-Driven Conversa...

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4th IEEE international conference on Digital Twins and Parallel Intelligence, DTPI 2024

作者： Du, Hongkai Li, Haozhou Peng, Qinke Fu, Laiyi Xi'an Jiaotong University School of Software Engineering Xi'an710049 China Xi'an Jiaotong University School of Automation Science and Engineering Xi'an710049 China Research Institute of Xi'an Jiaotong University Hangzhou311200 China Sichuan Digital Economy Industry Development Research Institute Chengdu610036 China

ISBN: (纸本)9798350349252

Generation-based conversational recommender systems (CRSs) are tailored to individual user needs, employing cutting-edge text generation techniques for generating contextually relevant and fluent responses. Nevertheless, current CRSs, including advanced general language models like ChatGPT, often overlook the subtle differences in users' linguistic styles and the evolving nature of their interests, which leads to a tendency for uniform responses at the onset of dialogues and unnecessarily prolonged interaction cycles. To overcome these challenges, our approach leverages user historical data and information from similar user profiles to more accurately capture the user linguistic feature within dialogue subtasks. We introduce an innovative multi-factor cross-attention mechanism, specifically designed for the efficient integration of diverse quantitative relationships. In the recommendation subtask, our strategy involves the intuitive representation of user interest migration trajectories and the stereotype. this not only allows for a more granular understanding of the trajectory of user interests but also alleviates data sparsity for new users. Our proposed framework, User Linguistic Style Awareness and Interest-Driven CRSs (LSAID), has undergone extensive testing on well-established CRS datasets such as ReDial and INSPIRED. the empirical results underscore LSAID's effectiveness, showcasing its state-of-the-art performance. Our code will be released at http://***/zhizhaixingchen/LSAID. © 2024 IEEE.

关键词： Recommender systems

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LabVIEW Integrated with Python Machine Learning for DC Motor Drive 4

LabVIEW Integrated with Python Machine Learning for DC Motor...

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4th international conference of science and Information Technology in Smart Administration, ICSINTESA 2024

作者： Poyai, Mana Maneetham, Dechrit Sutyasadi, Petrus Rajamangala University of Technology Thanyaburi Faculty of Technical Education Department of Mechatronics Engineering Pathumthani Thailand Vocational Faculty Sanata Dharma University Department of Mechatronics Yogyakarta Indonesia

ISBN: (纸本)9798350376111

this study presents a new technique that integrates LabVIEW and Python to enhance the control of DC motor drives through the utilization of machine learning methods. the objective of our research is to leverage LabVIEW's graphical programming interface in combination with the Scikit-learn Python package to train a Polynomial Regression model. the goal is to forecast Pulse Width Modulation (PWM) values at desired Rotations per Minute (RPM) of a DC motor. the accuracy and reliability of the trained model are evaluated using performance assessment metrics such as the R-squared (R2) coefficient of determination and the Root Mean Square Error (RMSE). the R2 score assesses the precision of the model's fit, whereas the RMSE quantifies the model's predictive capability by comparing observed and predicted data. this research also includes a comparison between the machine learning technique for DC motor drive and conventional PID control. the findings illustrate the successful integration of LabVIEW and Python in managing DC motor drives, achieving low overshooting and faster attainment of the set RPM point compared to classical PID control. this emphasizes machine learning's capacity to enhance control strategies;the application of Polynomial Regression yields promising results, demonstrating its effectiveness in predicting PWM and RPM values with an R2 value ranging from 96% to 98% and RMSE within 19. © 2024 IEEE.

关键词： Python

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