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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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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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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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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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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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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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Pondering About Task Spatial Misalignment: Classification-Localization Equilibrated Object Detection 48

Pondering About Task Spatial Misalignment: Classification-Lo...

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48th IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2023

作者： Zhang, Yudong Lu, Wei Wang, Xu Wang, Pengkun Wang, Yang Hefei China School of Software Engineering Ustc Hefei China School of Computer Science and Technology Ustc Hefei China School of Data Science Ustc Hefei China Suzhou Institute for Advanced Research Ustc Suzhou China

ISBN: (纸本)9781728163277

Object detection is a fundamental task in computer vision, consisting of both classification and localization tasks. Previous works mostly perform classification and localization with shared feature extractor like Convolution Neural Network. However, the tasks of classification and localization exhibit different sensitivities with regard to the same feature, hence the "task spatial misalignment"issue. This issue can result in a hedge issue between the performances of localizer and classifier. To address these issues, we first propose a novel Dynamic Coefficient Loss to simultaneously consider and balance the performances of classification and localization tasks. To well address anchor label misjudgment issue in irregular- shaped object detection, we define a new classification-aware IoU metric to assign anchors intelligently. Finally, we further introduce the localization factor into NMS by proposing a Classification-Localization balanced NMS. Extensive experiments on MS COCO and PASCAL VOC demonstrate that our proposals can improve RetinaNet by around 1.5% AP with various backbones. © 2023 IEEE.

关键词： Non-Maximum Suppression Object detection task spatial misalignment

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Graph-based Modeling and Simulation of Emergency Services Communication systems

Graph-based Modeling and Simulation of Emergency Services Co...

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International Symposium on Modeling, Analysis and Simulation of Computer and Telecommunication systems (MASCOTS)

作者： Jardi Martinez Jordan Michael Stiber Computing and Software Systems Division School of Science Technology Engineering and Mathematics University of Washington Bothell Bothell WA USA

ISBN: (数字)9798331531300

ISBN: (纸本)9798331531317

Emergency Services Communication systems (ESCS) are evolving into Internet Protocol based communication networks, promising enhancements to their function, availability, and resilience. This increase in complexity and cyber-attack surface demands better understanding of these systems’ breakdown dynamics under extreme circumstances. Existing ESCS research largely overlooks simulation and the little work that exists focuses primarily on cybersecurity threats and neglects critical factors such as non-stationarity of call arrivals. This paper introduces a robust, adaptable graph-based simulation framework and essential mathematical models for ESCS simulation. The framework uses a representation of ESCSes where each vertex is a communicating finite-state machine that exchanges messages along edges and whose behavior is governed by a discrete event queuing model. Call arrival burstiness and its connection to emergency incidents is modeled through a cluster point process. Model applicability is demonstrated through simulations of the Seattle Police Department ESCS. Ongoing work is developing GPU implementation and exploring use in cybersecurity tabletop exercises.

关键词： Adaptation models Protocols Law enforcement Computational modeling Stochastic processes Emergency services Mathematical models Telecommunications Internet Resilience

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Ppt4j: Patch Presence Test for Java Binaries 24

Ppt4j: Patch Presence Test for Java Binaries

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44th ACM/IEEE International Conference on software engineering, ICSE 2024

作者： Pan, Zhiyuan Hu, Xing Xia, Xin Zhan, Xian Lo, David Yang, Xiaohu Zhejiang University The State Key Laboratory of Blockchain and Data Security Hangzhou China Huawei Software Engineering Application Technology Lab Hangzhou China The Hong Kong Polytechnic University Hong Kong School of Computing and Information Systems Singapore Management University Singapore

ISBN: (纸本)9798400702174

The number of vulnerabilities reported in open source software has increased substantially in recent years. Security patches provide the necessary measures to protect software from attacks and vulnerabilities. In practice, it is difficult to identify whether patches have been integrated into software, especially if we only have binary files. Therefore, the ability to test whether a patch is applied to the target binary, a.k.a. patch presence test, is crucial for practitioners. However, it is challenging to obtain accurate semantic information from patches, which could lead to incorrect results. In this paper, we propose a new patch presence test framework named Ppt4j (Patch Presence Test forJava Binaries). Ppt4j is designed for open-source Java libraries. It takes Java binaries (i.e. bytecode files) as input, extracts semantic information from patches, and uses feature-based techniques to identify patch lines in the binaries. To evaluate the effectiveness of our proposed approach Ppt4j, we construct a dataset with binaries that include 110 vulnerabilities. The results show that Ppt4j achieves an F1 score of 98.5% with reasonable efficiency, improving the baseline by 14.2%. Furthermore, we conduct an in-the-wild evaluation of Ppt4j on JetBrains IntelliJ IDEA. The results suggest that a third-party library included in the software is not patched for two CVEs, and we have reported this potential security problem to the vendor. © 2024 ACM.

关键词： Open source software

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Humanoid Robot Collaborative Lifting Integrating Executable Judgment

Humanoid Robot Collaborative Lifting Integrating Executable ...

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2022 IEEE International Symposium on Product Compliance engineering - Asia, ISPCE-ASIA 2022

作者： Chang, Hua Yi, Pengfei Liu, Rui Dong, Jing Hou, Yaqing Zhou, Dongsheng The Key Laboratory of Advanced Design and Intelligent Computing Ministry of Education School of Software Engineering Dalian University Dalian China Dalian University of Technology Dalian China

ISBN: (数字)9798350332483

ISBN: (纸本)9798350332483

Humanoid robot collaborative lifting can be used in a variety of scenarios that require repetitive lifting tasks. Most existing studies of humanoid robot collaboration often assume that objects can always be lifted, which may result in damage to both the robot and the object if objects are too heavy to lift. To avoid such situations as much as possible, a collaborative lifting approach integrating executable judgment is proposed. First, a target search and localization method is constructed using monocular vision and marker points to identify the task object. Then, an executable judgment strategy is designed to determine whether the object is overweight or not according to robot force analysis. Finally, a multi-robot joint control model is proposed based on collaborative communication to perform collaborative tasks with different loads based on the judgment results. Experiments on two humanoid robots for different types and weights of targets show the effectiveness of the proposed approach. © 2022 IEEE.

关键词： Anthropomorphic robots

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