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17th IEEE International Conference on Industrial and Information systems, ICIIS 2023

作者： Ajikaran, Ramesh Hewarathna, Ashen Iranga Palanisamy, Vigneshwaran Joseph, Charles Thuseethan, Selvarajah Sabaragamuwa University of Sri Lanka Dept. of Computing and Infromation Systems Sri Lanka Sabaragamuwa University of Sri Lanka Dept. of Software Engineering Sri Lanka Fau Dept. of Computer Science Germany School of Information Technology Deakin University Australia

ISBN: (纸本)9798350323634

In the process of quality analysis of grains, number counting is one of the key steps for agricultural production. Traditional manual grain counting methods are time-consuming and subject to human error, and automated methods have the potential to improve accuracy and save time. In this study, we aimed to develop an image analysis-based method to automatically quantify the number of grains in a quicker manner. The 576 grain images were collected manually, and labelImg tagging tool used to annotate to generate a text file with their respective positions by drawing bounding boxes manually. The datasets consist were separated into three groups: training, validation, and test. For object detection, the YOLOv5, YOLOv4, and YOLOv3 algorithms represent cutting-edge deep learning frameworks. They replace the tedious and error-prone manual counting process by precisely identifying and counting grains in images obtained from agricultural fields. This technique helps to increase grain counting's precision and effectiveness. We believe this method will be extremely beneficial in guiding the development of high throughput systems for counting the number of grains in other crops as it performs well with a wide range of backgrounds, picture sizes, grain sizes, as well as various quantities of grain crowding. When compared to the other two approaches, YOLOv4 performed well in terms of accuracy, speed, and robustness (97.65%), demonstrating that the suggested strategy is competitive with other cutting-edge deep networkst. © 2023 IEEE.

关键词： Grain (agricultural product)

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Current Status and Analyses of the SPLICE Hazard Detection software

Current Status and Analyses of the SPLICE Hazard Detection S...

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AIAA Science and technology Forum and Exposition, AIAA SciTech Forum 2025

作者： Liu, John Y. Chen, Po-Ting Aldrich, Jack D. Bocchino, Robert L. Vaughan, Geoffrey Jet Propulsion Laboratory California Institute of Technology Payload/Instrument Pointing Control Systems Engineering Guidance and Control Section 4800 Oak Grove Drive PasadenaCA91109 United States Jet Propulsion Laboratory California Institute of Technology Guidance and Control Section 4800 Oak Grove Drive PasadenaCA91109 United States Jet Propulsion Laboratory California Institute of Technology Flight Software and Avionics Systems Section 4800 Oak Grove Drive PasadenaCA91109 United States

This paper provides a status report of the current development and analysis efforts for the hazard detection (HD) software for the Safe and Precise Landing-Integrated Capabilities Evolution (SPLICE) project. © 20... 详细信息

ISBN: (数字)9781624107238

ISBN: (纸本)9781624107238

关键词： Hazards

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FedCST: Federated Learning on Heterogeneous Resource-constrained Devices Using Clustering and Split Training 24

FedCST: Federated Learning on Heterogeneous Resource-constra...

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24th IEEE International Conference on software Quality, Reliability and Security Companion, QRS-C 2024

作者： Wang, Zhilu Lin, Haiyang Liu, Qi Zhang, Yonghong Liu, Xiaodong Nanjing University of Information Science and Technology School of Computer Science Jiangsu Nanjing China Jiangsu Province Engineering Research Center of Advanced Computing and Intelligent Services Jiangsu Nanjing China Nanjing University of Information Science and Technology School of Software Jiangsu Nanjing China Nanjing University of Information Science and Technology School of Automation Jiangsu Nanjing China Edinburgh Napier University School of Computing Edinburgh United Kingdom

ISBN: (纸本)9798350365658

With the rapid development of 5G and Internet of Things (IoT) technologies, edge devices such as sensors, smartphones, and wearable devices have become increasingly prevalent. The massive amount of distributed data generated by these devices offers unprecedented opportunities for deep learning, especially in fields like computer vision and edge computing. However, this data often exists in isolated data silos, and due to its privacy-sensitive nature, it cannot be directly processed in a centralized manner. Additionally, the data heterogeneity and devices heterogeneity further exacer-bates the challenges in federated learning (FL). Moreover, with the implementation of data privacy protection regulations, user demand for privacy protection is growing significantly. Therefore, how to effectively utilize dispersed data for distributed collaborative learning while ensuring data privacy is a pressing issue that needs to be addressed in the field of FL. In response to these challenges, this work has conducted research on clustering and segmentation FL algorithms for heterogeneous resource-constrained devices. A lightweight homogeneous device clustering strategy has been designed, which incorporates a split learning (SL) mechanism to enhance the accuracy and training efficiency of FL models. This approach reduces the load on resource-constrained devices and improves privacy security. Consequently, this method can meet the dual requirements of privacy protection and computational efficiency in real-world scenarios. © 2024 IEEE.

关键词： Federated learning

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software Practice in Small software Companies: Development Context Constraints on Process Adoption. 22

Software Practice in Small Software Companies: Development C...

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Proceedings of the 2022 European Symposium on software engineering

作者： Micheal Tuape Victoria Hasheela-Mufeti Petrus Iiyambo Anna Kayanda Sharon Mensah Jussi Kasurinen Department of Software Engineering Lappeenranta-Lahti University of Technology Finland Dept of Computing Maths and Statistical Sciences University of Namibia Namibia Dept of Information Systems College of Business Education Tanzania Software Department B-Systems Limited Ghana

ISBN: (纸本)9781450397308

Small software companies (SSCs) interact with the immediate environment, exposing them to challenges that force the organization to undertake adjustments if it must survive and remain in business. These adjustments result into counterproductive practices and changes that create complexities in process adoption. This cross-sectional survey investigates the occurrences around the customer in the development context that affect the adoption of process in SSCs. To answer the research questions, we conducted a survey on 115 respondents and found out that although customer engagement has a significant relationship with reducing rework, inadequacies in the engagement due to the customer's lack of knowledge of software processes, triggers unstructured and ad-hoc methods in SSCs. The main contribution of this paper is a customer engagement framework that seeks to transform software processes by focusing on the customer as a pillar of achieving purpose and value to reduce development effort and time.

关键词： Small software Companies software processes software Development Context

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CrossMeta: A Fast and Cheap Cross-Metaverse Interoperability Protocol

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IEEE Transactions on Services Computing 2025年

作者： Li, Taotao Yang, Qinglin Huang, Huawei Zhu, Xuanye Sun, Zhu Liu, Yuan Zheng, Zibin Sun Yat-Sen University School of Software Engineering Zhuhai campus Guangdong Zhuhai519082 China Guangzhou University Cyberspace Institute of Advanced Technology Huangpu campus Guangdong Guangzhou510006 China Singapore University of Technology and Design Singapore

Metaverse is drawing increasing attention from both academia and industry. Interoperability among different metaverse systems has become essential. A cross-metaverse interoperability protocol can enable interoperability across metaverses. However, cross-metaverse protocols often suffer significant cost overhead and transaction latency. For example, in STYLE, a leading cross-metaverse platform, 74% of transaction latency and 97% of the cost overhead are attributed to the relay blockchain rather than the two participating metaverses. To make cross-metaverse efficient, in this paper, we propose a fast and cheap cross-metaverse interoperability protocol named CrossMeta. CrossMeta can enable direct communication among heterogeneous metaverses rather than depending on a relay blockchain. This is achieved through two components: i) a committee that relays transactions from the source metaverse to the destination metaverse, along with availability proofs, and ii) a smart contract that verifies the proofs provided by the committee. To ensure an honest majority within the selected committee, we propose a dynamic committee selection method based on the chain quality property. Furthermore, we demonstrate that honest brokers achieve a Nash equilibrium. Additionally, we prove that the proposed CrossMeta protocol satisfies the security properties of atomicity and liveness. To demonstrate the practicality of CrossMeta, we implemented a prototype of the CrossMeta using two real-world metaverse platforms, i.e., Axie Infinity and Sandbox. The evaluation results show that CrossMeta outperforms other cross-metaverse solutions regarding transaction latency and gas fees. © 2008-2012 IEEE.

关键词： Blockchain

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A Review on the Process of Automated software Testing

arXiv

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arXiv 2022年

作者： Shree, Durga N. Dharinya, Sree S. Vijayasree, Dasari Roopa, Nadendla Sai Arun, Anugu Department of Software and Systems Engineering School of Information Technology and Engineering Vellore Institute of Technology Vellore India

The requirements in automation, digitalization, and fast computations have loaded the IT sector with expectations of highly reliable, efficient, and cost-effective software. Given that the process of testing, verification, and validation of software products consumes 50-75% of the total revenue if the testing process is ineffective, "n" times the expenditure must be invested to mend the havoc caused. A delay in project completion is often attributed to the testing phase because of the numerous cycles of debugging process. The software testing process determines the face of the product released to the user. It sets the standard and reliability of a company's outputs. As the complexity increases, testing gets intense so as to examine all the outliers and various branches of the processing flow. The testing process is automated using software tools to avoid the tedious manual process of test input generation and validation criteria, which certifies the program only to a certain confidence level in the presence of outliers. © 2022, CC BY.

关键词： software testing

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Analysis of the Impacts of Consuming Red and Processed Meat on Colorectal Cancer and the role of Machine Learning in Clinical Diagnostics

Analysis of the Impacts of Consuming Red and Processed Meat ...

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Intelligent and Innovative Technologies in Computing, Electrical and Electronics (IITCEE), International Conference

作者： C. Navaneethan S. Thanga Prasath Department of Software and Systems Engineering Vellore Institute of Technology Vellore Tamil Nadu India Vellore Institute of Technology Vellore Tamil Nadu India

Phenomenal advancement in modernistic technology has allowed researchers to make any desire they can imagine come true. However, even in today's sophisticated technology environment, the medical world is desperately trying to decrease the tremendous mortality caused by cancer every year. Furthermore, they are constantly investigating if the food we eat daily affects the impact of cancer. Therefore, in this systematic analysis, we reviewed the daily diet, which is widely suspected of causing changes in the effects of cancer. It also expresses the tremendous growth that has occurred in the medical industry due to the advent of artificial intelligence and machine learning. Intake of meat and its variants (red and processed meat) is believed to raise cancer hazards; however, data to support this proof has been contradictory. Consequently, a comprehensive review and meta-analysis of the evidence linking red meat, processed meat, and its subgroups to colorectal cancer (CRC) incidence were conducted. CRC hazards are associated with red and processed meat ingestion. Conversely, whole grains, veggies, fruits, cruciferous vegetables (cabbage and broccoli), milk, dairy products, eggs, and white meat have been related to a low risk of CRC. Surprisingly, our findings demonstrate that traditional diets in many countries are related to a lower risk of all forms of cancer.

关键词： Industries Dairy products Systematics Machine learning Hazards Medical diagnostic imaging Cancer

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Patch Target Guided Dual-Branch Deep Multiple Instance Learning for 3D MRI Analysis

Patch Target Guided Dual-Branch Deep Multiple Instance Learn...

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2024 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2024

作者： Dai, Mingzhe Shi, Xiaoshuang Zhu, Xiaofeng Pan, Tingrui Li, Kang University of Science and Technology of China School of Software Engineering Hefei China West China Hospital of Sichuan University West China Biomedical Big Data Center Chengdu China University of Electronic Science and Technology of China School of Computer Science and Engineering Chengdu China Sichuan Artificial Intelligence Research Institute Yibin China University of Science and Technology of China Suzhou Institute for Advanced Research Suzhou China Shanghai Artificial Intelligence Laboratory Shanghai China

ISBN: (纸本)9798350386226

Deep multiple instance learning (MIL) has attracted considerable attention in medical image analysis, since it only requires image-level labels for model training without using fine-grained (or patch) annotations. Unfortunately, MIL-based methods might lose some significant patch features. Although pseudo-label-based methods, which assign a pre-defined label to each patch, can explore more patch-level features, they might bring label noise and make the patch-level features lose diversity, thereby possibly restricting the model performance. To overcome this issue, we propose a novel gradient-based patch target generation (PTG) module to dynamically produce a feature vector for each patch as its target. Additionally, based on the PTG module, we propose a patch-target guided dual-branch deep MIL framework for 3D MRI data analysis, where both the two branches consist of a CNN model to extract patch-level features, an attention module to interpret the significance of patches, and a bag-level classifier, while the second branch also contains the PTG module to generate patch targets of patches. Moreover, the two branches are alternatively updated in our framework, resulting in a bi-level optimization problem, and thus we design a bi-level optimization algorithm to solve our proposed objective function. Extensive experiments demonstrate the superior classification and interpretation performance of the proposed framework over recent state-of-the-art methods. Codes are available at https://***/daimz1213/mil2024. © 2024 IEEE.

关键词： Image annotation

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Image Classification Analysis to Identify the Tourism Potential Sukabumi on Instagram Using A Deep Learning Approach

Image Classification Analysis to Identify the Tourism Potent...

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International Conference on Informatics and Computing (ICIC)

作者： Falentino Sembiring Rieska Rahayu Ayuningsih Muhamad Muslih Muhammad Andy Maulana Gina Wahdayani Futri Mohd Zainuri Saringat Rachma Siva Ainunnisa Anggun Fergina Information Systems Nusa Putra University Sukabumi Indonesia Department of Software Engineering Faculty of Computer Science and Information Technology Universiti Tun Hussein Onn Malaysia

ISBN: (数字)9798331517601

ISBN: (纸本)9798331517618

This research focuses on image classification, grouping images based on shared characteristics. The study aims to identify the highest-potential category within various fields, from high to low potential. By understanding existing potential, researchers can inform targeted strategies for future development. The methodology combines Logistic Regression in Orange Data Mining to enhance classification accuracy. Hierarchical Clustering is used to analyze 7,777 image results. Findings reveal that Tourism (C3) has the most potential, followed by Culinary (C2) and Culture (C4), while Handcrafts (C1) exhibit the least potential. In conclusion, logistic regression successfully identifies influential aspects of Sukabumi through Instagram images, aiding potential development

关键词： Deep learning Logistic regression Accuracy Web sites Multimedia communication Data mining Cultural differences Informatics Image classification

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GLADformer: A Mixed Perspective for Graph-level Anomaly Detection

arXiv

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arXiv 2024年

作者： Xu, Fan Wang, Nan Wu, Hao Wen, Xuezhi Zhang, Dalin Lu, Siyang Li, Binyong Gong, Wei Wan, Hai Zhao, Xibin School of Software Engineering Beijing Jiaotong University Beijing China University of Science and Technology of China Hefei China Advanced Cryptography and System Security Key Laboratory of Sichuan Province Chengdu China BNRist KLISS School of Software Tsinghua University Beijing China

Graph-Level Anomaly Detection (GLAD) aims to distinguish anomalous graphs within a graph data set. However, current methods are constrained by their receptive fields, struggling to learn global features within the graphs. Moreover, most these methods are based on spatial domain and lack exploration of spectral characteristics. In this paper, we propose a multi-perspective hybrid graph-level anomaly detector named GLADformer, consisting of two key modules. Specifically, we first design a Graph Transformer module with global spectrum enhancement, which ensures balanced and resilient parameter distributions by fusing global features and spectral distribution characteristics. Furthermore, to explore local anomalous attributes, we customize a band-pass spectral GNN message passing module that enhances the model’s generalization capability. Through comprehensive experiments on ten real-world datasets from multiple domains, we validate the effectiveness and robustness of GLADformer. This demonstrates that GLADformer outperforms current state-of-the-art graph-level anomaly detection methods, particularly in effectively capturing global anomaly representations and spectral characteristics. Copyright © 2024, The Authors. All rights reserved.

关键词： Distribution transformers

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