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On the Influence of Grid Cell Size on Taxi Demand Prediction 8th

On the Influence of Grid Cell Size on Taxi Demand Predict...

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8th EAI International Conference on Smart Objects and Technologies for social Goods, GOODTECHS 2022

作者： Korth, Merlin Schleibaum, Sören Müller, Jörg P. Ehlers, Rüdiger Department of Informatics Clausthal University of Technology Julius-Albert-Straße 4 Clausthal-Zellerfeld38678 Germany Institute for Software and Systems Engineering Clausthal University of Technology Julius-Albert-Straße 4 Clausthal-Zellerfeld38678 Germany

ISBN: (纸本)9783031288128

Accurate taxi demand prediction has the potential to increase customer satisfaction and hence the usage of ride-sharing by predicting the number of taxis needed at a certain place and time. When reviewing the related work on demand prediction, we observed that in taxi demand prediction different grid topologies – e.g. rectangular subdivisions of an area – and sizes are applied. However, it is not clear how and why the grid cells are configured the way they are and a systematic comparison of different topologies and sizes as regards their influence on urban demand prediction is lacking. In this paper, we compare the influence of different grid cell sizes – 250 m, 500 m, and 1000 m – on the prediction accuracy of different types of deep learning-based taxi demand prediction approaches, such as convolutional neural networks, recurrent neural networks, and graph neural networks. Therefore, we select five deep learning-based approaches from related work and evaluate their performance on the New York City TLC taxi trip dataset and three different evaluation metrics. Our results show that approaches with a grid cell of size 1000 m and 500 m achieve a higher prediction accuracy. Furthermore, we propose to consider the grid cell size as a tunable parameter in demand prediction models. © 2023, ICST Institute for Computer Sciences, Social Informatics and Telecommunications engineering.

关键词： Recurrent neural networks

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Intelligent Recognition of Geometric Tolerance Symbol Annotation Blocks

Intelligent Recognition of Geometric Tolerance Symbol Annota...

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Artificial Intelligence and Intelligent Manufacturing (AIIM), International Symposium on

作者： Congxin Li Yuanping Xu Chaolong Zhang Chao Kong Jin Jin Weiye Wang Zhijie Xu Benjun Guo Dan Tang School of Software Engineering Chengdu University of Information Technology Chengdu China School of Advanced Technology Xi’an Jiaotong-Liverpool University Suzhou China

ISBN: (数字)9798331541729

ISBN: (纸本)9798331541736

In previous mechanical design drawings, the geometric tolerance symbol annotation block was directly annotated on the mechanical design drawing by engineers, resulting in the symbol annotation block being non-editable and non-shareable. To achieve digitization, this study proposes an improved Swin_YOLOv5_GE model based on YOLOv5 to realize the positioning of the symbol annotation block. Compared with the original YOLOv5, the accuracy is increased by 4%. Then, image processing is performed on the located and cropped symbol annotation block to achieve denoising and tilt correction of the symbol annotation block. Finally, it is input into CRNN for the recognition of the symbol annotation block. The final experimental results prove that the method in this paper can obtain an overall recognition accuracy of 94%, proving the feasibility of this method.

关键词： YOLO Accuracy Annotations Noise reduction Noise Symbols Mechanical variables measurement Mechanical products Manufacturing Mechanical engineering

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MRUIE: Multi-Reference Mapping for Underwater Image Enhancement

MRUIE: Multi-Reference Mapping for Underwater Image Enhancem...

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International Joint Conference on Neural Networks (IJCNN)

作者： Tonglin Cheng Juan Li Jintao Luo Zhihui Li Faculty of Information Technology Beijing University of Technology Beijing China Beijing Engineering Research Center for loT Software and Systems Beijing China

ISBN: (数字)9798350359312

ISBN: (纸本)9798350359329

In complex underwater environments, the image quality will be damaged due to light absorption, water quality and other factors, which brings certain challenges to the underwater object detection task. In order to improve the quality of under-water images, as well as the effectiveness of underwater detection tasks, we proposed a multi-reference mapping based image enhancement network (MRUIE). Previous image enhancement methods set only one reference image for the original image to be learned, ignoring the diversity of real underwater images. We optimized for the salient problems of underwater images by allowing the network to learn multiple reference images to capture the uncertainty of real underwater images. First, for the prominent problems of color distortion, contrast imbalance, and low brightness and darkness in underwater images, we designed three optimization paths to generate three reference images respectively. Then, the three reference images are fed into the feature extraction network to construct the statistical distribution of features and generate a series of potential enhancement distributions. Finally, based on the enhancement distributions, Monte Carlo likelihood estimation is used to determine the final enhancement results. Experiments conducted on two datasets demonstrate that our proposed image enhancement algorithm can effectively enhance the restoration of the original underwater images and provide significant performance improvement in underwater vision tasks.

关键词： Image quality Monte Carlo methods Image color analysis Statistical distributions Water quality Feature extraction Image restoration

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A Metamodel for a DSL to Design Decision Support systems in Smart Tourism

A Metamodel for a DSL to Design Decision Support Systems in ...

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Decision Aid Sciences and Application (DASA), International Conference on

作者： Charaf Ouaddi Lamya Benaddi Adnane Souha Lahbib Naimi El mahi Bouziane Abdeslam Jakimi Department of Computer Sciences Software Engineering and Information Systems Engineering Team Faculty of Science and Technology Errachidia Moulay Ismail University Morocco

ISBN: (数字)9798350369106

ISBN: (纸本)9798350369113

The development of Decision Support systems (DSS) for several companies operating in sectors such as tourism, healthcare, or others, presents significant challenges due to the nature of their multi-component architecture. A typical DSS consists of a data component, a model component incorporating machine learning and deep learning techniques, and a user interface component, each one requires diverse technologies. This complexity increases development time and effort, hindering the efficient design of DSS tailored to specific domains like tourism, where real-time decision-making is essential. One promising approach to overcome these challenges is the use of Domain-Specific Languages (DSLs). DSLs allow developers to model and generate code more efficiently by providing high-level abstractions suited to the domain. By leveraging DSLs, it is possible to simplify the design process and automate the generation of code or parts of the DSS, thereby reducing the overall complexity and time required for development. This paper proposes a metamodel for designing DSS, specifically for the Smart Tourism sector. The metamodel serves as a foundation for the creation of DSL. The goal is to provide a solution to facilitate the development of DSS for the demands of tourism. The paper outlines the key concepts, approach, and results, presenting the first element of the proposed DSL which is the metamodel capturing the essential components and relationships for DSS in Smart Tourism.

关键词： Decision support systems Codes Source coding Medical services User interfaces Syntactics Real-time systems Complexity theory DSL software engineering

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Computer-Aided Potato Disease Detection by Using Deep Learning Techniques

Computer-Aided Potato Disease Detection by Using Deep Learni...

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International Conference on Green Energy, Computing, and Intelligent technology, GEn-CITy 2023

作者： Razaq, Fareeha Bilal, Muhammad Ramzan, Muhammad Naveed, Muhammad Razzaq, Samreen Department of Computer Science University of Sargodha Sargodha Pakistan Department of Computing and Information Systems School of Engineering and Technology Sunway University Petaling Jaya Malaysia Department of Software Engineering University of Sargodha Sargodha Pakistan School of Electronics and Computer Science University of Southampton Malaysia Iskandar Puteri Malaysia

ISBN: (纸本)9789819998326

Potato is the most widely grown and consumed food throughout the world. There are a number of potato crop diseases that affect production, and these diseases differ in symptoms, circumstances, and controls. Early detection and recognition of disease information can aid in disease prevention and production. This paper presents deep learning models using proposed CNN and pre-trained models for potato disease detection and classification. The proposed model is more efficient and accurate at detection and classification. To perform classification, we used two datasets: PLD and PlantVillage. The Xception CNN model serves as the foundation for our proposed model. It achieved 1.00 accuracy, 0.99 precision, 1.00 recall, and 0.99 F1-score on the PLD dataset. On the PlantVillage dataset, it achieved 1.00 accuracy, 1.00 precision, 1.00 recall, and 1.00 F1-score. We also compared the results of Inception-ResNet-V2, MobileNet-V2, VGG-19, Inception-V3, and Xception models with the performance of our proposed model. For three classes of potato leaves, the proposed CNN model produced more accurate results than other pre-trained models. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Classification (of information)

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Point Cloud Implicit 3D Reconstruction Based on Adaptive Threshold Optimal Transport

Point Cloud Implicit 3D Reconstruction Based on Adaptive Thr...

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Artificial Intelligence and Intelligent Manufacturing (AIIM), International Symposium on

作者： Qinyun Li Jin Jin Yuanping Xu Chao Kong Weiye Wang Zhijie Xu Chaolong Zhang Benjun Guo School of Software Engineering Chengdu University of Information Technology Chengdu China School of Advanced Technology Xi’an Jiaotong-Liverpool University Suzhou China

ISBN: (数字)9798331541729

ISBN: (纸本)9798331541736

To achieve high precision reconstruction of complex point cloud data, this paper presents a novel method based on an adaptive threshold. Firstly, a pre trained neural network is used to initially segment the point cloud, and an adaptive threshold is introduced into the discrete optimal transport to effectively detect feature regions such as edges and corners based on local geometric information. Subsequently, a zero level set smoothing loss function and a normal alignment loss function are introduced into the implicit neural network to optimize the feature region and the smooth region respectively. Finally, the optimized point cloud is reconstructed through the Marching Cubes algorithm. Experimental results show that compared with DeepSDF and Occupancy Networks, the accuracy of the proposed method on the corresponding data sets is improved by 3.33% and 1.2% respectively.

关键词： Point cloud compression Surface reconstruction Three-dimensional displays Adaptive systems Smoothing methods Accuracy Image edge detection Feature detection Neural networks Feature extraction

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An Improved SlowFast Model for Violence Detection

An Improved SlowFast Model for Violence Detection

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Artificial Intelligence and Intelligent Manufacturing (AIIM), International Symposium on

作者： Qinxue Huang Chaolong Zhang Yuanping Xu Weiye Wang Zhijie Xu Benjun Guo Jin Jin Chao Kong School of Software Engineering Chengdu University of Information Technology Chengdu China School of Advanced Technology Xi’an Jiaotong-Liverpool University Suzhou China

ISBN: (数字)9798331541729

ISBN: (纸本)9798331541736

As social safety concerns become increasingly prominent, the importance of violence detection has become increasingly significant. Accurately identifying violent behavior helps facilitate timely interventions, reducing harm and losses, thereby enhancing public safety. To minimize the interference from irrelevant actions (e.g., those from spectators and uninvolved individuals) during violence detection, this study proposes an improved SlowFast method that enhances the robustness of the model by modifying the network structure of the Slow Pathway and lateral connections. In the Slow Pathway, the original residual block is replaced with an SE-Res residual block, which strengthens the weighting of channel importance, making the model more stable in complex scenarios. The lateral connections are improved by integrating a 3D-Convolutional Block Attention Module (3D-CBAM), enhancing feature fusion and increasing the model’s sensitivity to key features across different temporal scales. Furthermore, to address the class imbalance issue within the dataset, the Focal Loss function has been modified, effectively improving the performance of each class. The improved model demonstrates excellent performance in violence detection tasks, achieving an accuracy of $\mathbf{9 6. 6 7 \%}$, which is a $\mathbf{1. 1 2 \%}$ improvement over the classical model.

关键词： Training Visualization Accuracy Sensitivity Computational modeling Interference Robustness Public security Manufacturing Sports

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Identifying and Documenting Best Practices in Digital Transformation 23

Identifying and Documenting Best Practices in Digital Transf...

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Proceedings of the 28th European Conference on Pattern Languages of Programs

作者： Mohammad Yusuf Momand Valentino Vranić Institute of Informatics Information Systems and Software Engineering Faculty of Informatics and Information Technologies Slovak University of Technology in Bratislava Slovakia

ISBN: (纸本)9798400700408

Digital transformation allows organizations to maintain sustainable development and address ongoing challenges. The current digital transformation and advanced technology mega-trend significantly impact society and organizations, making digital technology crucial for public and private organizations, universities, and daily life. Despite the rising demand, there is often a lack of standard policies governing the use of digital technology. Practical experience and literature reveal key challenges in transitioning from traditional to digital systems, including limited public awareness, mindset, ICT skills, government support, and collaboration between organizations and software engineering societies. Governments must establish digital transformation policies and offer software development guidelines for public organizations to digitalize their systems. Additionally, they should provide or support research projects funded by governmental and nongovernmental organizations to identify infrastructure and regulatory issues within Afghanistan’s digital transformation context. This study documents six organizational patterns observed during the transformation of traditional systems into digital systems, illustrating their sequence and explaining the development of a pattern language from these patterns.

关键词： digital transformation organizational patterns patterns software engineering

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Patterns for Improving User Contribution 23

Patterns for Improving User Contribution

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Proceedings of the 28th European Conference on Pattern Languages of Programs

作者： Mohammad Daud Haiderzai Valentino Vranić Institute of Informatics Information Systems and Software Engineering Faculty of Informatics and Information Technologies Slovak University of Technology in Bratislava Slovakia

ISBN: (纸本)9798400700408

User contributions is an important part in software development, the pursuit of identifying effective means of user contributions in software development is a crucial area of interest for software companies and their development teams. Determining the most opportune moments, locations, and methods for such contributions presents a significant challenge for organizations, which necessitates the exploration of generic and proven solutions. Applying patterns, established and observed solutions may provide the answers to such challenges and similar ones. This paper offers insights into the practical implementation of seven newly discovered organizational patterns in a real-life environment. The patterns are comprehensively described, accompanied by illustrative examples, and subsequently applied in actual practices.

关键词： organizational patterns software development software engineering

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A Lightweight Convolutional Dual-Channel Transformer Framework for Efficient Part Segmentation of Point Cloud Data

A Lightweight Convolutional Dual-Channel Transformer Framewo...

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Artificial Intelligence and Intelligent Manufacturing (AIIM), International Symposium on

作者： Jianhua Li Yuanping Xu Chaolong Zhang Chao Kong Jin Jin Weiye Wang Zhijie Xu Benjun Guo Dan Tang School of Software Engineering Chengdu University of Information Technology Chengdu China School of Advanced Technology Xi’an Jiaotong-Liverpool University Suzhou China

ISBN: (数字)9798331541729

ISBN: (纸本)9798331541736

In recent years, transformer-based models have made significant breakthroughs in natural language processing and computer vision. However, these models have encountered challenges when dealing with point cloud data because the irregular and disordered structure of point cloud data leads to a huge computational and memory burden. To address this problem, this paper proposes a Multidimensional ConvolutionDual Channel Transformer Network for efficient processing of point cloud data. The MCDTN framework consists of two branches: the main channel enhances the modeling of cross-channel features through dynamic attention to optimize feature representation; the auxiliary channel further improves fine-grained segmentation capabilities through encoders, multi-scale information interaction, and spatial attention. Experimental results show that MCDTN performs excellently in shape classification and part segmentation tasks, effectively reducing computational costs.

关键词： Point cloud compression Adaptation models Convolution Shape Computational modeling Semantics Transformers Feature extraction Natural language processing Manufacturing

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