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2022 International Conference on Computer Network Security and software engineering, CNSSE 2022

作者： Xu, Fengjuan He, Haitong Gao, Jianlei Gong, Tianyu Li, Yaobing Li, Yun China Industrial Control Systems Cyber Emergency Response Team 35 Lugu Road Beijing100040 China China Agricultural University College of Information and Electrical Engineering 17 Qinghua East Road Beijing100083 China

ISBN: (纸本)9781510655980

The continuously updated and widely applied information technology and internet technology make the amount of data increase with an unimaginable speed. Subsequently, industries have also entered into the "Digital Times", which brings challenges and opportunities for industrialization. Through industrial big data government technology, we can continuously improve the level of industrial intelligence, the management efficiency and the production efficiency of enterprises, so as to improve the competitiveness of enterprises and the country. However, in the face of this large number of industrial big data with complex and diverse data characteristics, the application of traditional data processing methods in industrial big data government is not applicable, especially in the condition of valuable and scarce computing resources. A new proper data process framework, in this paper, is proposed for industrial data, whose principle is to reasonably select data features by utilizing a max-relevance min-redundancy (mrmr) algorithm that considering the relationship between data characteristics. The effect is verified on a series of industrial datasets. And the experiments results can effectively improve the utilization and analysis efficiency of data, reduce the difficulty of data processing, improve the data storage efficiency and reduce the data storage space. © 2022 SPIE.

关键词： Big data

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Blood Oxygen Saturation Estimation from Fingertip Videos Using Residual Convolutional Neural Network

Blood Oxygen Saturation Estimation from Fingertip Videos Usi...

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Electronic information engineering and Computer Communication (EIECC), International Conference on

作者： Yuting Yang Jiong Yu Zheng Chu Xinlong Feng College of Software Xinjiang University Xinjiang China School of Information Science and Engineering Xinjiang University Xinjiang China College of Mathematics and Systems Science Xinjiang University Xinjiang China

ISBN: (数字)9798331534622

ISBN: (纸本)9798331534639

This paper proposes a novel method for estimating peripheral blood oxygen saturation (SpO 2 ) using RGB signals extracted from fingertip videos captured by a smartphone camera. SpO 2 is a critical indicator for assessing blood oxygen levels, which can significantly decrease in respiratory conditions such as asthma, chronic obstructive pulmonary disease (COPD), and COVID-19. Inspired by the photophysiological model, this method employs a convolutional neural network (CNN) with cascaded residual blocks to extract RGB signals from fingertip videos for SpO 2 estimation. Compared to facial videos, which are susceptible to motion artifacts and ambient light variations, fingertip video signals are more stable. Experimental results on two public datasets demonstrate that the proposed method effectively estimates SpO 2 and outperforms comparable methods. The feasibility and accuracy of SpO 2 estimation based on RGB signals were also validated.

关键词： Accuracy Oxygen Computational modeling Estimation Predictive models Physiology Spatiotemporal phenomena Convolutional neural networks Blood Videos

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DiLaAg - digitalisation and innovation laboratory in agricultural sciences 10

DiLaAg - digitalisation and innovation laboratory in agricul...

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10th European Conference on Precision Livestock Farming, ECPLF 2022

作者： Pichlbauer, B. Chapa-Gonzalez, J.-M. Purcell, W. Mallinger, K. Koppensteiner, L. Kitzler, F. Pejakovic, V. Medel-Jimenez, F.J. Krexner, T. Iwersen, M. Neubauer, T. Neugschwandtner, R. Bauer, A. Kral, I. Motsch, V. Korth, F. Drillich, M. Rauber, A. Tjoa, A.M. Kaul, H.-P. Gronauer, A. Clinical Unit for Herd Health Management in Ruminants University Clinic for Ruminants Department for Farm Animals and Veterinary Public Health University of Veterinary Medicine Vienna Austria Information and Software Engineering Group Institute of Information Systems Engineering University of Technology Vienna Austria Institute of Agronomy Department of Crop Sciences University of Natural Resources and Life Sciences Vienna Austria Institute of Agricultural Engineering Department of Sustainable Agricultural Systems University of Natural Resources and Life Sciences Vienna Austria

ISBN: (纸本)9788396536006

Sustainable production and development in livestock sectors are critical to meeting the growing requirements placed on it, increased production, efficiency and reduced labour, along with the various political declarations and societal claims. Digitalisation is increasingly seen as a prerequisite to achieving the goal of sustainable agriculture and reduced labour. Therefore, it is the aim of the DiLaAg project to bring together scientists from different fields of expertise to work interdisciplinary on the complex topic of digitalisation in agriculture. In the DiLaAg consortium, PhD students and their supervisors from the University of Natural Resources and Life Sciences, Vienna (BOKU), the University of Technology Vienna (TU) and the University of Veterinary Medicine Vienna (VETMED) build the scientific core. The project was initiated and is funded by the Federal State of Lower Austria and the private foundation ‘Forum Morgen’. The PhD students work in the fields of robotics, image-based plant classification, agronomic analyses of crops, impact of technologies, digital twinning, livestock management on pasture and sustainability assessment as well as on interdisciplinary topics such as computer vision, application of digital sensor technologies, machine learning, complexity science, remote sensing, data integration, precision agriculture and precision livestock farming. Apart from this scientific nucleus, the DiLaAg consortium aims to act as a platform for innovation and exchange, by continuously expanding its network and collaborations in order to continue working on current challenges in agriculture. © ECPLF 2022. All rights reserved.

关键词： Sustainable development

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Improved Ingredients-based Recipe Recommendation software using Machine Learning

Improved Ingredients-based Recipe Recommendation Software us...

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IEEE International Conference on Intelligent Computing and information systems (ICICIS)

作者： Mariam Ashraf Khadija Tarek Naglaa Fathy Mariam Mahmoud Rana Emad Khadija Khaled Noran Amr Software Engineering Department Faculty of Computer and Information Science Ain Shams University Cairo Egypt Information Systems Department Faculty of Computer and Information Science Ain Shams University Cairo Egypt

In this paper we propose an improved recipe recommendation system that employs image recognition of food ingredients. The system is currently a mobile application that performs image recognition on uploaded or camera-captured images and recommends recipes containing the recognized ingredients. We used the ResNet-V2 architecture to build a convolutional neural network model for image recognition, which was able to identify 33 different food ingredients with an accuracy rate of 89%. The recommendation system uses the identified ingredient labels, as well as user preferences and restrictions, to display a list of recipes containing the identified ingredients. This feature allows users to discover new and exciting recipes based on the ingredients they currently have at home, without having to worry about dietary restrictions or other preferences. Overall, our system provides a convenient and personalized way for users to discover and prepare delicious meals based on their unique needs and preferences.

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SYSTEMATIC LITERATURE REVIEW ON APPLICATION OF LEARNING-BASED APPROACHES IN CONTINUOUS INTEGRATION

arXiv

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arXiv 2024年

作者： Arani, Ali Kazemi Le, Triet Huynh Minh Zahedi, Mansooreh Babar, M. Ali CREST-The Centre for Research on Engineering Software Technologies University of Adelaide AdelaideSA5005 Australia School of Computing and Information Systems University of Melbourne MelbourneVIC3010 Australia

Context: Machine learning (ML) and deep learning (DL) analyze raw data to extract valuable insights in specific phases. The rise of continuous practices in software projects emphasizes automating Continuous Integration (CI) with these learning-based methods, while the growing adoption of such approaches underscores the need for systematizing knowledge. Objective: Our objective is to comprehensively review and analyze existing literature concerning learning-based methods within the CI domain. We endeavour to identify and analyse various techniques documented in the literature, emphasizing the fundamental attributes of training phases within learning-based solutions in the context of CI. Method: We conducted a Systematic Literature Review (SLR) involving 52 primary studies. Through statistical and thematic analyses, we explored the correlations between CI tasks and the training phases of learning-based methodologies across the selected studies, encompassing a spectrum from data engineering techniques to evaluation metrics. Results: This paper presents an analysis of the automation of CI tasks utilizing learning-based methods. We identify and analyze nine types of data sources, four steps in data preparation, four feature types, nine subsets of data features, five approaches for hyperparameter selection and tuning, and fifteen evaluation metrics. Furthermore, we discuss the latest techniques employed, existing gaps in CI task automation, and the characteristics of the utilized learning-based techniques. Conclusion: This study provides a comprehensive overview of learning-based methods in CI, offering valuable insights for researchers and practitioners developing CI task automation. It also highlights the need for further research to advance these methods in CI. © 2024, CC BY.

关键词： Automation

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Deepfake detection using deep learning methods: A systematic and comprehensive review

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Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery 2024年第2期14卷 e1520-e1520页

作者： Heidari, Arash Jafari Navimipour, Nima Dag, Hasan Unal, Mehmet Department of Software Engineering Haliç University Istanbul Turkey Department of Computer Engineering Kadir Has Universitesi Istanbul Turkey Future Technology Research Center National Yunlin University of Science and Technology Douliou Taiwan Management Information Systems Kadir Has Universitesi Istanbul Turkey Department of Computer Engineering Nisantasi Universitesi Istanbul Turkey

Deep Learning (DL) has been effectively utilized in various complicated challenges in healthcare, industry, and academia for various purposes, including thyroid diagnosis, lung nodule recognition, computer vision, large data analytics, and human-level control. Nevertheless, developments in digital technology have been used to produce software that poses a threat to democracy, national security, and confidentiality. Deepfake is one of those DL-powered apps that has lately surfaced. So, deepfake systems can create fake images primarily by replacement of scenes or images, movies, and sounds that humans cannot tell apart from real ones. Various technologies have brought the capacity to change a synthetic speech, image, or video to our fingers. Furthermore, video and image frauds are now so convincing that it is hard to distinguish between false and authentic content with the naked eye. It might result in various issues and ranging from deceiving public opinion to using doctored evidence in a court. For such considerations, it is critical to have technologies that can assist us in discerning reality. This study gives a complete assessment of the literature on deepfake detection strategies using DL-based algorithms. We categorize deepfake detection methods in this work based on their applications, which include video detection, image detection, audio detection, and hybrid multimedia detection. The objective of this paper is to give the reader a better knowledge of (1) how deepfakes are generated and identified, (2) the latest developments and breakthroughs in this realm, (3) weaknesses of existing security methods, and (4) areas requiring more investigation and consideration. The results suggest that the Conventional Neural Networks (CNN) methodology is the most often employed DL method in publications. According to research, the majority of the articles are on the subject of video deepfake detection. The majority of the articles focused on enhancing only one parameter, w

关键词： National security

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Hierarchical Continual Learning for Domain-Knowledge Retention in Healthcare Federated Learning

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IEEE Transactions on Consumer Electronics 2025年

作者： Iqbal, Saeed Zhong, Xiaopin Khan, Muhammad Attique Wu, Zongze AlHammadi, Dina Abdulaziz Liu, Weixiang Choudhry, Imran Arshad Shenzhen University College of Mechatronics and Control Engineering Shenzhen518060 China Prince Mohammad Bin Fahd University Department of AI Alkhobar Saudi Arabia Princess Nourah bint Abdulrahman University College of Computer and Information Sciences Department of Information Systems Riyadh11671 Saudi Arabia University of Central Punjab Faculty of Information Technology and Computer Science Department of Software Engineering Lahore Pakistan

Internet of Medical Things (IoMT) applications encounter issues with data protection, continual adaptation, and domain-specific knowledge retention, especially in consumer-centric IoMT scenarios. We overcome these obstacles and facilitate effective knowledge retention and task adaptation in IoMT applications. This study attempts to create a unique privacy-preserving federated learning framework that combines a hierarchical learning structure with Continual Learning (CL). Despite the advancements in Federated Learning (FL), current models have trouble integrating changing datasets in real-time while protecting privacy, as well as catastrophic forgetting, which occurs when previously learned knowledge is lost when adjusting to new tasks. We present a hierarchical learning framework that makes use of three levels of models -Junior Model (JM), Consultant Model (CM), and Senior Consultant Model (SCM) -to overcome these drawbacks. Each level of the model aids in archived retention and domain-knowledge adaptation. To guarantee that the model maintains valuable information over time and adapts to new tasks with ease, our method blends domain adaptation strategies with ongoing learning approaches like knowledge distillation and elastic weight consolidation (EWC). We compare the suggested methodology with current state-of-the-art (SOTA) models on healthcare datasets for tasks like illness diagnosis and medical image categorization. According to our findings, the hierarchical continual learning model performs better than SOTA techniques in terms of accuracy, task adaptability, and privacy protection. In the healthcare industry, our study sets a new standard for privacy-preserving, continuously adaptable federated learning systems, allowing for real-time, scalable IoMT applications that can adapt dynamically to a variety of changing datasets. © 1975-2011 IEEE.

关键词： Patient treatment

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Runtime Backdoor Detection for Federated Learning via Representational Dissimilarity Analysis

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IEEE Transactions on Dependable and Secure Computing 2025年

作者： Zhang, Xiyue Xue, Xiaoyong Du, Xiaoning Xie, Xiaofei Liu, Yang Sun, Meng University of Oxford Department of Computer Science United Kingdom Peking University Department of Information and Computing Science School of Mathematical Science China Monash University Department of Software Systems and Cybersecurity Australia Singapore Management University School of Computing and Information Systems Singapore Nanyang Technological University School of Computer Science and Engineering Singapore

Federated learning (FL), as a powerful learning paradigm, trains a shared model by aggregating model updates from distributed clients. However, the decoupling of model learning from local data makes FL highly vulnerable to backdoor attacks, where a single compromised client can poison the shared model. While recent progress has been made in backdoor detection, existing methods face challenges with detection accuracy and runtime effectiveness, particularly when dealing with complex model architectures. In this work, we propose a novel approach to detecting malicious clients in an accurate, stable, and efficient manner. Our method utilizes a sampling-based network representation method to quantify dissimilarities between clients, identifying model deviations caused by backdoor injections. We also propose an iterative algorithm to progressively detect and exclude malicious clients as outliers based on these dissimilarity measurements. Evaluations across a range of benchmark tasks demonstrate that our approach outperforms state-of-the-art methods in detection accuracy and defense effectiveness. When deployed for runtime protection, our approach effectively eliminates backdoor injections with marginal overheads. © 2025 IEEE.

关键词： Federated learning

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Using transformers and Bi-LSTM with sentence embeddings for prediction of openness human personality trait

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PeerJ Computer Science 2025年 11卷 1-42页

作者： Naz, Anam Khan, Hikmat Ullah Alsahfi, Tariq Alhajlah, Mousa Alshemaimri, Bader Daud, Ali Department of Information Technology University of Sargodha Punjab Sargodha Pakistan Department of Information Systems and Technology College of Computer Science and Engineering University of Jeddah Jeddah Saudi Arabia Applied of Computer Science College King Saud University Riyadh Saudi Arabia Software Engineering Department College of Computing and Information Sciences King Saud University Riyadh Saudi Arabia Faculty of Resilience Rabdan Academy Abu Dhabi United Arab Emirates

Understanding human personality traits is significant as it helps in decision making related to consumers’ behavior, career counselling, team building and top candidates’ selection for recruitment. Among various traits, openness is essential as it shows both diverse aspects of sensitive nature or intuitive nature. The individuals having a sensing nature tends to be more practical and prefer to focus on concrete information whereas the users having intuitive trait type is characterized by a focus on abstract ideas, creative thinking and future-oriented perspectives. In this research work, we aim to explore diverse natural language processing (NLP) based features and apply state of the art deep learning algorithms for openness trait prediction. Using standard Myers-Briggs Type Indicator (MBTI) dataset, we propose the use of the latest deep features of sentence embeddings which captures contextual semantics of the content to be used with deep learning models. For comparison, we explore textual features of Frequency-Inverse Document (TF-IDF) and parts of speech (POS) tagging with machine learning models and deep features of word2vec and global vectors for word representation (GloVe) with deep learning models. The comprehensive empirical analysis reveals that TF-IDF used with gradient boosting achieves high accuracy of 90% whereas, the deep feature of sentence embeddings when used and with deep model bidirectional long short-term memory (Bi-LSTM) achieves 90.5% accuracy. The best results have been achieved using the latest Transformer-based DistilBERT, which achieves the highest accuracy of 92% outperforming the existing studies in relevant literature. Subjects Human-Computer Interaction, Artificial Intelligence, Data Mining and Machine Learning, Natural Language and Speech © 2025 xxxx

关键词： Deep learning

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Few-shot Object Detection Algorithm Based on YOLOv8-Mamba-Like Linear Attention 16

Few-shot Object Detection Algorithm Based on YOLOv8-Mamba-Li...

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16th International Conference on Communication software and Networks, ICCSN 2024

作者： Wang, Hongliang Wang, Kaizhu Ji, Binglong Deng, Changyi Shenyang Institute of Computing Technology Chinese Academy of Sciences University of Chinese Academy of Sciences Liaoning Province Human- Computer Interaction System Engineering Research Center Based on DigitTwin Shenyang China Shenyang Institute of Computing Technology Chinese Academy of Sciences University of Chinese Academy of Sciences Shenyang China Institute of Software China Industrial Control Systems Cyber Emergency Response Team Beijing China

ISBN: (纸本)9798331506322

In the production and assembly process, the accuracy of detection is usually low due to limited resources and small quantities. In this paper, we propose a few-shot learning method for object detection based on the combination of YOLOv8 and Mamba-Like Linear Attention (MLLA) module. This method utilizes a high-pixel camera to capture the shape and size of the workpiece. By consistency-based mutual augmentation structure for few-shot samples data augmentation. By introducing the efficient and lightweight MLLA module, the model's speed and accuracy in object detection and segmentation tasks on mobile devices are significantly improved. Specifically, MLLA mainly integrates the two key elements of 'forget gate' and 'block design' in Mamba, and MLLA replaces forget gate by using position coding (RoPE) to provide necessary location information while maintaining parallel computation and fast inference speed. This makes MLLA improves the accuracy of detection and segmentation recognition. Experimental results show that compared to the original YOLOv8 model, the model with the MLLA module exhibits improvements in segmentation precision and recognition accuracy. This method demonstrates high accuracy and robustness in workpiece recognition, making it suitable for real-time industrial applications. © 2024 IEEE.

关键词： Object detection

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