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检索条件"任意字段=6th International Conference on the Art, Science, and Engineering of Programming, Programming 2022"

共 1074 条记录，以下是311-320 订阅

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SSTCU:A Spatial-Temporal Correlation Unit based Traffic Flow Prediction Approach 6

SSTCU:A Spatial-Temporal Correlation Unit based Traffic Flow...

引用

6th IEEE international conference on Universal Village, UV 2022

作者： Liu, Yao Chen, Xiyu Jin, Zhongfu Zhang, Yujie Yang, Jiandang Intelligent Transportation Institute of Zhejiang Communications Investment Group Co. Ltd Hangzhou China Polytechnic Institute Zhejiang University Hangzhou China Zhejiang University Collge of Control Science and Engineering Hangzhou China

ISBN: (纸本)9781665474771

Traffic flow prediction is a crucial application in traffic guidance and control. Existing approaches rarely consider the dynamically correlated spatial-temporal features between multiple road segments. To effectively capture the spatial-temporal features between multiple road segments, we propose a novel approach, Spatial-Temporal Correlation Unit (STCU). STCU utilizes a fast Fourier transform-based autocorrelation mechanism to extract the correlations between temporal sequences, a graph attention mechanism to extract the correlations between spatial traffic monitors, and a feedforward neural network to fuse the interacting spatial-temporal correlations. We construct a traffic flow prediction model with a stacked STCU module called Sequential STCU (SSTCU). We conduct a lot of experiments and compared them with the several baselines to verify the effectiveness of SSTCU. the results show that the proposed method outperforms the baselines and achieves state-of-the-art performance. We also conduct ablation experiments to verify the effectiveness of the STCU module. Moreover, we change the layer depth of the model to find the most efficient setting for a computation efficiency consideration. © 2022 IEEE.

关键词： Feedforward neural networks

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Genetic Optimization of Variable Bias Double Cross-limit Combustion Control Strategy for Industrial Boilers

Genetic Optimization of Variable Bias Double Cross-limit Com...

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Internet of things, Automation and artificial Intelligence (IoTAAI), international conference on

作者： Bao Liu Naiqing Zhou Zaiying Wang College of Electrical & Control Engineering Xi'an University of Science and Technology Xi'an China

ISBN: (数字)9798350386974

ISBN: (纸本)9798350386981

For the bias function used in the variable bias double cross limit control system, a linear variation method is adopted. In order to obtain the optimal bias value, in this paper the intelligent genetic algorithm (GA) is used to search the suitable offset parameters, and the optimized parameters are applied in the varying-offset cross-limiting combustion control system to achieve the control target of furnace combustion. the validation results show that not only the response speed of the system is improved, but also the optimal combustion interval that the excess air coefficients are maintained.

关键词： Simulation Furnaces Control systems Linear programming Genetics Boilers Combustion Internet of things Optimization Genetic algorithms

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Angular Position Estimation for Human-Following and Robot Navigation 6th

Angular Position Estimation for Human-Following and Robot Na...

引用

6th international conference on Smart Grid and Internet of things, SGIoT 2022

作者： Asante, Isaac theng, Lau Bee Tsun, Mark Tee Kit Faculty of Engineering Computing and Science Swinburne University of Technology Sarawak Kuching Malaysia

ISBN: (纸本)9783031312748

Mobile robot navigation and human following are two related areas under the field of robotics that have garnered a lot of interest over the years, due to their advantages in the real world, in various settings. Modern techniques depending exclusively on computer vision for environmental data input commonly rely on state-of-the-art object detection methods to obtain data about the location of detected objects in a scene. However, these detection frameworks do not directly provide the angular position of obstacles and human targets in images. In this research project, the Mask R-CNN instance segmentation framework detects static objects and humans in an environment. A chain of algorithms is then used to transform the image’s content and pixel information into a one-dimensional array that can be mapped to the robot’s field of view. the findings show that the result can aid a mobile robot in estimating the angular position of obstacles and a human in a scene, which is necessary for collision-free navigation and robot-human following in an unknown environment. the proposed method also shows adaptivity as it works outdoors and indoors under poor lighting conditions. It can be used as a standalone algorithm in robotics simulations with webcams and static images. © 2023, ICST Institute for Computer sciences, Social Informatics and Telecommunications engineering.

关键词： Object detection

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Application to Enhance the programming Knowledge of Children

Application to Enhance the Programming Knowledge of Children

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Advancements in Computing (ICAC), international conference on

作者： Anuga Dissanayake Vishwa Lakpriya Mahendran Sanjeevan Ahamed Fahmi Samanthi E.R. Siriwardana Mihiri Samaraweera Dept. of Computer Science and Software Engineering Sri Lanka Institute of Information Technology Malabe Sri Lanka Dept. of Information Technology Sri Lanka Institute of Information Technology Malabe Sri Lanka

ISBN: (数字)9798331517878

ISBN: (纸本)9798331517885

this proposal outlines the development of a comprehensive educational platform aimed at bridging the Information Technology knowledge gap among Tamil-speaking students in Sri Lanka. the platform is designed to enhance programming skills through personalized quizzes, hands-on projects, and adaptive learning models. the application focuses on two primary components: the first component emphasizes project-based learning and assessments, leveraging advanced artificial Intelligence models for generating educational content. this model is combined with Retrieval-Augmented Generation, which retrieves relevant external information to refine and tailor the educational content to each student's needs. this component keeps students engaged and challenged at appropriate levels, promoting deeper understanding and practical Information Technology skills. the second component involves a Level-based IT Fundamental Knowledge Evaluation system, which incorporates machine learning algorithms to predict student performance, recommend customized learning paths, and assess overall technical knowledge. the platform also integrates the OpenAI Application programming Interface, which provides real-time Tamil translation, making the content more accessible to the target demographic. the platform empowers educators to better address individual student needs with real-time feedback and tailored experiences. Its innovative approach enhances Information Technology education, equipping Sri Lankan students with essential programming skills to succeed globally.

关键词： Adaptation models Translation Machine learning algorithms Computational modeling Retrieval augmented generation Learning (artificial intelligence) Real-time systems Proposals Information technology programming profession

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Development of the complementary method to solve fractional linear programming problems. 6

Development of the complementary method to solve fractional ...

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6th international Scientific conference for Iraqi Al Khwarizmi Society, FISCAS 2021

作者： Jaber, Waleed Khalid Hassan, Isra'A H. Khraibet, Tahani Jabbar Computer Science and Mathematics University of Thi-Qar Iraq Engineering Communications University of Technology Iraq Department of Mathematics Thi-Qar Directorates of Education

In this paper we development of the complementary method to Solve Fractional Linear programming Problems (FLPP), Instead of taking the Numerator and denominator elements and set them in in tables as D1 and D2, then calculating its value and the value of (Ze), hence we could minimize the tables and calculations in this method quickly and with fewer tables. © Published under licence by IOP Publishing Ltd.

关键词： Linear programming

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Analyzing Students’ Learning Engagements Using PLS-SEM: A Case Study in A Small Private Online programming Course 22

Analyzing Students’ Learning Engagements Using PLS-SEM: A C...

引用

Proceedings of the 2022 6th international conference on Education and E-Learning

作者： Bilegjargal Daramsenge Nien-Lin Hsueh Lien-Chi Lai Department of Information Engineering and Computer Science Feng Chia University Taiwan

ISBN: (纸本)9781450398428

Nowadays, particularly during the epidemic situation, the increasing popularity of online-offline education. In computer science education, a small private online programming course (SPOPC) is an important curriculum course to enhance the students’ problem-solving, creative thinking and teamwork activities. the main purpose of this study is to investigate the influences of students’ composite engagements (learning performances and personal feelings) in a SPOPC. We applied an online engagement framework for higher education and partial least squares structural equation modeling (PLS-SEM) approach. Our analysis was conducted from a Python programming course at Feng Chia University, Taiwan, held in the fall semester of 2021–2022 through OpenEdu platform. Our results show that (1) Strongest mediator performance was ‘Video watching’ affected by four significant relations; (2) Behavioral engagement ‘BE’ and emotional engagement ‘EE’ are positively affected ‘Video watching’ performance and they were inextricably linked factors; (3) Social engagement ‘SE’ significantly affected the ‘Term project’ and ‘Final score’ through ‘Midtest’; (4) Cognitive engagement ‘CE’ did not significantly affect the ‘Term project’; Most important is that (5) Students’ first feelings about the course (the first part of the semester) are important factors in deciding to continue a related course or drop out of the course in the future. these findings have valuable practical implications for course instructors who design and lead the programming course.

关键词： programming course PLS-SEM Distance learning Composite engagements

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Generative Cooperative Network for Person Image Generation 6

Generative Cooperative Network for Person Image Generation

引用

6th IEEE international conference on Universal Village, UV 2022

作者： Liu, Yang Wang, Shuai Wu, Yubin Sheng, Hao Xiong, Zhang Beihang University State Key Laboratory of Virtual Reality Technology and Systems School of Computer Science and Engineering Beijing China

ISBN: (纸本)9781665474771

Person image generation is to synthesize realistic pedestrian images that follow the same distribution as the given dataset. Previous attempts can be generally categorized into two classes: some methods use human pose information as guidance and others try to generate person images from scratch. the former is to transfer the pose of a source image to a reference pose. the generated person image have the same identity as the source image. the latter takes a random noise from latent space as input, and the real person images are only used as references for the discriminator. While pose-guided person image generation is widely studied, generating-from-scratch methods are also worth exploring because they can synthesize person image with new identity, which is a useful manner of data augmentation. these two types of generating methods have their different advantages and disadvantages, and sometimes they are complementary. In this work, the authors design a Generative Cooperative Network (GCN) to jointly train two types of GANs. the two GANs serve different purposes, and can learn from each other during the cooperative learning procedure. the proposed approach is verified on public datasets, and the results show that our GCN improves the performance of the baseline methods. Comparisons with state-of-the-art methods also prove the effectiveness of the proposed method. © 2022 IEEE.

关键词： Computer vision

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Self-Train: Self-Supervised On-Device Training for Post-Deployment Adaptation 6

Self-Train: Self-Supervised On-Device Training for Post-Depl...

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6th IEEE international conference on Smart Internet of things, SmartIoT 2022

作者： Liu, Jinhao Yu, Xiaofan Rosing, Tajana University of California Computer Science and Engineering La Jolla San DiegoCA United States

ISBN: (数字)9781665479523

ISBN: (纸本)9781665479523

Recent years have witnessed a significant increase in deploying lightweight machine learning (ML) on embedded systems. the list of applications range from self-driving vehicles to smart environmental monitoring. However, the performance of ML models after the deployment degrades because of potential drifting of the device or the environment. In this paper, we propose Self-Train, a self-supervised on-device training method for ML models to adapt to post-deployment drifting without labels. Self-Train employs offline contrastive feature learning and online drift detection with self-supervised adaptation. Experiments on images and real-world sensor datasets demonstrate consistent accuracy improvements over state-of-the-art online unsupervised methods with 2.45× at maximum, while maintaining lower execution time with a maximum of 32.7× speedup. © 2022 IEEE.

关键词： Embedded systems

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Relationship Between Handedness and Cognition Performance of University Undergraduates 6th

Relationship Between Handedness and Cognition Performance of...

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6th Kuala Lumpur international conference on Biomedical engineering, BioMed 2021

作者： Tan, Yin Qing Tee, Si Yun Ooi, Hong Kiat Department of Mechatronics and Biomedical Engineering Universiti Tunku Abdul Rahman Selangor Sungai Long Malaysia Center for Healthcare Science and Technology Universiti Tunku Abdul Rahman Selangor Sungai Long Malaysia

ISBN: (纸本)9783030907235

Human were born to be left-or right-handers. Left-handedness is rare and only consists of around 10% of population. Some lefties facing problems in daily life and may need to declare their left-handedness as one of the disabilities, however, there are also some lefties show excel in music and mathematics. One of the common theories about left-handers is their brain are structure differently than common right-handers, and thus resulted in different cognition ability. this study aimed to investigate the relationship between handedness and cognition performance among 108 young adults in a local university using MATRICS Consensus Cognitive Battery. Results are considered to support the hypothesis that handedness may have significant impact on two cognition tests, Symbol Coding and Maze test, with p-value 0.04 and 0.005, respectively. there was no significant difference has been found on Trail Making Test, Working Memory and Social Cognition test. this study suggested that right-handed participants able to complete the timed cognition tests faster than left-handers which indicates their better ability in speed of processing and problem-solving skills. © 2022, Springer Nature Switzerland AG.

关键词： Computer programming

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Automated Test Input Generation for Testing Representational State Transfer (REST) Application programming Interface (API) using Parameter Fuzzing

Automated Test Input Generation for Testing Representational...

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IEEE international conference on Knowledge Innovation and Invention (ICKII)

作者： Chien-Hung Liu Shu-Ling Chen Hong-Kai Huang Department of Computer Science and Information Engineering National Taipei University of Technology Taipei Taiwan Department of Industrial Management and Information Southern Taiwan University of Science and Technology Tainan Taiwan

Fuzz testing has been widely used for generating inputs automatically to test the stability and security of RESTful web services. To be effective, fuzzing input generation needs to provide both valid and invalid input data. One way to automatically generate fuzzy inputs to test Representational State Transfer (REST) Application programming Interface (API) is based on the data types and structures of the API parameters. However, even with such information, it is still challenging to automatically generate effective fuzzy inputs. thus, we proposed a method to generate fuzzy inputs for API parameters according to their categories instead of generating the inputs randomly. An API parameter was classified into a corresponding category according to the parameter’s information first. Based on the classified category, associated rules were used to generate fuzzy inputs for the parameter automatically. the proposed method has been implemented and integrated into RESTler, a well-known open-source fuzzing tool for REST APIs. Experimental results showed that, as compared with random input generation, the proposed method had wider API status code coverage and better error detection results.

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