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Multi-layered retrieval integrity verification mechanism for blockchain oracle

Peer-to-Peer Networking and Applications 2025年第4期18卷 1-18页

作者： Wu, Rui Chen, Long Song, Guangmin Xu, Yang Wang, Xinlong School of Computer Science School of Cyber Science and Engineering Engineering Research Center of Digital Forensics Ministry of Education Nanjing University of Information Science and Technology Nanjing 210044 China China Unicom (Zhejiang) Industrial Internet Co. Ltd Hangzhou 311199 China School of Software Shandong University Jinan 250100 China

In a typical distributed network such as blockchain, blockchain oracles play the role of a bridge between off-chain data and on-chain smart contracts. However, blockchain oracles themselves cannot guarantee the security of data outside the blockchain. Since external data sources outside the blockchain are usually not controlled by the blockchain network, malicious attackers may tamper with data or forge information, thereby affecting the execution of smart contracts and causing inaccurate or erroneous contract results. To address this issue, we propose a blockchain oracle multi-layer retrieval integrity verification mechanism, which combines the Shamir threshold signature scheme for double verification to ensure the integrity of external data source data. At the same time, the mechanism combines the Merkle hash tree with the signature chain structure to construct a multi-layer cascade verification framework, which further ensures the security of external data sources. In addition, we also proposed a data source reputation management system, which uses a scoring mechanism to evaluate the accuracy and immediate responsiveness of historical data to enhance the reliability of data sources within the oracle framework. Both theoretical analysis and experimental results show that the proposed method effectively solves the problem of insufficient data source reliability. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2025.

关键词： Blockchain oracle Retrieval integrity verification Shamir threshold signature

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Realization of Network Security Knowledge Base with Knowledge Graph 3rd

Realization of Network Security Knowledge Base with Knowledg...

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3rd International Conference on Machine Learning, Cloud Computing and Intelligent Mining, MLCCIM 2024

作者： Ouyang, Zepeng Li, Xiangyang Wang, Bing Ji, Zhenyan School of Software Engineering Beijing Jiaotong University No. 3 Shangyuan Village Beijing China Cyberspace Security Department China Railway Information Technology Group Co. Ltd. No. 10 Fuxing Avenue Beijing China

ISBN: (纸本)9789819616978

In the era of digital transformation, the sheer volume of network security data poses significant challenges in terms of organization and retrieval. Traditional search engines fail to provide contextual answers, necessitating the development of more advanced systems. This paper presents the design and implementation of a network security knowledge base enhanced with a knowledge graph. The proposed system aims to improve the efficiency of information management and retrieval for network security professionals. We discuss the multi-layer architecture of the knowledge base, which includes the UI, service, data access, and data storage layers. The knowledge graph is constructed through a four-layer architecture comprising the data, extraction, knowledge, and application layers. Using the Scrapy framework, we efficiently crawl and extract data from various online sources. This data is processed with the Jena tool and stored in Neo4j as structured triples, enabling complex queries and advanced analytic. Our system supports semantic queries, knowledge association discovery, and intelligent question answering, improving user experience and decision-making in network security management. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Knowledge graph

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The Impact of Blockchain technology for Evaluation of Electronic Medical Certification

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SN Computer Science 2025年第5期6卷 1-14页

作者： Azam Zia, M. Anwar, Munazza Mehmood, Faisal Razzaq, Abdul Asif, Muhammad Department of Computer Science University of Agriculture Faisalabad Punjab Faisalabad 38000 Pakistan Department of Civil and Transportation Engineering Shenzhen University Shenzhen 518060 China Computer Science Muhammad Nawaz Shareef University of Agriculture Punjab Multan 59300 Pakistan Department of Computer and Software Engineering Information Technology University (ITU) Punjab Lahore Pakistan

Evaluation of Electronic Medical Certification (EMC) is integral to today's healthcare systems since they are a consolidated database of individual patients' medical histories. Security measures and privacy issues are standard in electronic medical certification. Incompatible systems make sharing data across healthcare providers difficult, which may lead to data breaches and a lack of patient authority over their medical data. Blockchain has become essential for real-world corporate applications where confidentiality of information is paramount. The handling of security in medical records through a decentralized peer-to-peer network might be revolutionized by blockchain technology and improving the protection of the interchange of healthcare records. This study proposes a lightweight flask framework-based blockchain strategy to store and display patient status and transaction log information about their disease diagnosis. In a potential Blockchain system, the diseased patients act as assets, and their records contain any prior illnesses as blocks. The report analyzes a lot of national and international EHR standards. It identifies which of these interoperability needs to be addressed, discusses the interoperability challenges in but also outlines the standards. Current blockchain-based EHR frameworks. Without the suggested has the requirement for centralized systems. By using this framework, the healthcare industry can be provided safer ways to while also providing the exchange of health information. It offers features of immutability, security, and user control over stored records. This work contributes to an understanding of what potential Blockchain technology has offered an interoperable application in the EHR frameworks. Meet the blockchain-based EHR framework. Multiple national and international EHR standards. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2025.

关键词： Blockchain Cryptography Decentralized Electronic medical certification (EMC) Transaction

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Flexible ViG: Learning the Self-Saliency for Flexible Object Recognition

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IEEE Transactions on Circuits and Systems for Video technology 2025年

作者： Yang, Kunshan Zuo, Lin Jing, Mengmeng Tian, Xianlong He, Kunbin Ding, Yongqi University of Electronic Science and Technology of China School of Information and Software Engineering ChengDu611731 China University of Electronic Science and Technology of China School of Computer Science and Engineering ChengDu611731 China

Existing computer vision methods mainly focus on the recognition of rigid objects, whereas the recognition of flexible objects remains unexplored. Recognizing flexible objects poses significant challenges due to their inherently diverse shapes and sizes, translucent attributes, ambiguous boundaries, and subtle inter-class differences. In this paper, we claim that these problems primarily arise from the lack of object saliency. To this end, we propose the Flexible Vision Graph Neural Network (FViG) to optimize the self-saliency and thereby improve the discrimination of the representations for flexible objects. Specifically, on one hand, we propose to maximize the channel-aware saliency by extracting the weight of neighboring graph nodes, which is employed to identify flexible objects with minimal inter-class differences. On the other hand, we maximize the spatial-aware saliency based on clustering to aggregate neighborhood information for the centroid graph nodes. This introduces local context information and enables extracting of consistent representation, effectively adapting to the shape and size variations in flexible objects. To verify the performance of flexible objects recognition thoroughly, for the first time we propose the Flexible Dataset (FDA), which consists of various images of flexible objects collected from real-world scenarios or online. Extensive experiments evaluated on our FDA, FireNet, CIFAR-100 and ImageNet-Hard datasets demonstrate the effectiveness of our method on enhancing the discrimination of flexible objects. © 1991-2012 IEEE.

关键词： Graph neural networks

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Towards Trustworthy Aircraft Safety: Explainable AI for Accurate Incident and Accident Predictions 20th

Towards Trustworthy Aircraft Safety: Explainable AI for Accu...

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20th International Conference on the Economics of Grids, Clouds, Systems, and Services, GECON 2024

作者： Amin, Maryam Noor, Umara Fatima, Manahil Rashid, Zahid Altmann, Jörn Department of Software Engineering Faculty of Computing and Information Technology International Islamic University Islamabad44000 Pakistan Technology Management Economics and Policy Program College of Engineering Seoul National University Seoul Korea Republic of College of Engineering Seoul National University Seoul Korea Republic of Integrated Major in Smart City Global Convergence Seoul National University Seoul Korea Republic of

ISBN: (纸本)9783031812255

Despite technological advancements, ensuring aircraft safety remains a challenge, however, Machine learning (ML)-based approaches for predicting future incidents play a crucial role in addressing flight safety. As ML models increase in complexity, their decision-making process becomes less transparent, posing significant challenges to trustworthiness. While simpler models demonstrate lower accuracy, more intricate models such as deep neural networks achieve higher accuracy but sacrifice interpretability. In this study, we enhance trustworthiness in aircraft safety prediction by leveraging a dataset of past accidents and incidents to prevent similar accidents from occurring in the future. To achieve this, we apply Random Forest and Extreme Gradient Boosting models to classify different categories of aircraft incidents. Additionally, we apply two powerful explainable artificial intelligence (XAI) techniques: Local Interpretable Model-Agnostic Explanations (LIME) and Shapley Additive exPlanations (SHAP) to provide insights into both local and global predictions made by the models. Notably, our results reveal high accuracy in these predictions while maintaining trustworthiness. This research contributes to the advancement of XAI and offers valuable insights for safety-critical applications and decision support systems. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Aircraft accidents

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An examination of critical factors influencing consumers' behavior in live streaming e-commerce 24

An examination of critical factors influencing consumers' be...

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5th International Conference on Computer Science and Management technology, ICCSMT 2024

作者： Chen, Jian Sun, Wenying Zhao, Wenjin School of Software Engineering Chengdu University of Information Technology Sichuan Chengdu China Cox School of Business Southern Methodist University Dallas United States School of Computing and Artificial Intelligence Southwestern University of Finance and Economics Sichuan Chengdu China

ISBN: (纸本)9798400709999

The rapid development of live streaming e-commerce calls for a deeper understanding of the factors influencing consumer *** paper uses web crawling to gather data, combining Latent Dirichlet Allocation (LDA) and word frequency analysis for feature *** regression is applied to validate critical factors influencing consumer behavior in live streaming *** data covers March to September 2023, including 361.68 hours of live streaming, 3, 093, 402 words of anchor speeches, and 4, 095, 309 records of consumer *** critical factors under six dimensions from three perspectives were identified and the combination of three perspectives has a role in influencing consumer *** of critical factors influenced consumer behavior, with the most significant impacts of the anchor's positivity and promotion, the product's delivery service, and the number of bullet screens in the *** paper highlights the combined influence of anchor, product, and scene perspectives on consumer behavior, providing valuable insights for optimization. © 2024 Copyright held by the owner/author(s). Publication rights licensed to ACM.

关键词： Web crawler

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Design of PMCW Millimeter Wave Radar Algorithms on an Embedded DSP 16

Design of PMCW Millimeter Wave Radar Algorithms on an Embedd...

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16th International Conference on Signal Processing Systems, ICSPS 2024

作者： Zhao, Jinghao Liu, Qiuming Tan, Bin School of Software engineering Jiangxi University of Science and Technology Nanchang China Nanchang Key laboratory of Virtual Digital Factory and Cultural Communications Nanchang China Jiangxi Provincial Key Laboratory of Electronic Data Control and Forensics China School of Electronics and Information Engineering Jinggangshan University Ji’an China

ISBN: (纸本)9781510689251

The rapid advancements in autonomous driving technology necessitate the extensive deployment of automotive radars operating within the 77-81 GHz millimeter-wave band in the forthcoming years. In contrast to earlier Frequency Modulated Continuous Wave (FMCW) radars, Phase-Modulated Continuous Wave (PMCW) radars exhibit notable improvements in processing speed and flexibility, offering superior range and velocity resolution. These enhancements are critical for the precise detection and interpretation of dynamic and complex traffic scenarios. This paper initially presents the simulation and testing of algorithms for range and speed measurement using single-input single-output (SISO) PMCW millimeter-wave radar. Building upon these results, further simulations incorporating multi-input multi-output (MIMO) systems utilizing Hadamard codes are conducted to augment PMCW radar performance. The final phase of this study involves implementing the relevant signal processing algorithms on a custom-developed digital signal processor (DSP) named SWIFT. Experimental findings demonstrate that PMCW radar significantly mitigates multipath effects and clutter, maintaining high performance in complex environments. Furthermore, the algorithms executed on the DSP meet the anticipated performance standards. The proposed methodology not only validates the theoretical framework but also establishes a foundation for future hardware implementation. © 2025 SPIE.

关键词： Automotive radar

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An intelligent framework for sarcasm detection in Arabic tweets using deep learning with Al-Biruni earth radius optimization algorithm

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Alexandria engineering Journal 2025年 127卷 562-572页

作者： AL-anazi, Reema G. A. Alzaidi, Muhammad Swaileh Eltahir, Majdy M. Almukadi, Wafa Sulaiman Alajmani, Samah Hazzaa Darem, Abdulbasit A. Alshahrani, Mohammed Alhebaishi, Nawaf Department of Arabic Language and Literature College of Humanities and Social Sciences Princess Nourah bint Abdulrahman University P.O. Box 84428 Riyadh11671 Saudi Arabia Department of English Language College of Language Sciences King Saud University P. O. Box 145111 Riyadh Saudi Arabia Department of Information Systems Applied College at Mahayil King Khalid University Saudi Arabia Department of Software Engineering College of Engineering and Computer Science University of Jeddah Saudi Arabia Department of Information Technology College of Computers and Information Technology Taif University P.O. Box 11099 Taif21944 Saudi Arabia Department of Computer Science College of Science Northern Border University Arar Saudi Arabia Computer Department Applied College Najran University Najran66462 Saudi Arabia Department of Information Systems Faculty of Computing and Information Technology King Abdulaziz University Jeddah Saudi Arabia

Social media networks play a major role in expressing people's feelings, reviews, and thoughts. One of the popular linguistic patterns to express or criticize one's ideas with ridicule is sarcasm, where they have unintended and intended meanings in written text. The sarcastic text is used to reverse the polarity of the sentiment. As a result, sarcasm detection in the text positively affects the sentimental analysis (SA) tasks and improves accuracy. The sarcasm recognition of Arabic content is constrained even though Arabic is the most popular language for web content sharing, and yet still naive owing to various challenges, such as the insufficient data sources, the multiple dialects, and the morphological structure of the Arabic language. This study presents an Artificial Intelligence Driven Sarcasm Detection using Optimal Deep Learning (AIDSD-ODL) technique on Arabic tweets. In the AIDSD-ODL technique, a hyperparameter-tuned DL model is utilized to identify sarcastic or non-sarcastic Arabic tweets. At the initial stage, the AIDSD-ODL method undergoes data pre-processing to convert the input tweets into a compatible format. In the next phase, the Glove word embedding process is used in the AIDSD-ODL technique. The self-attention bidirectional long short-term memory (SA-BiLSTM) model is utilized for sarcasm detection. The Al-Biruni Earth Radius (BER) model is implemented for the hyperparameter selection process to enhance the performance of the SA-BiLSTM network. The experimental results of the AIDSD-ODL method are examined under the Arabic tweets dataset. The comparison study of the AIDSD-ODL method portrayed a superior accuracy value of 94.20 % over existing models. © 2025 The Authors

关键词： Deep learning

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Enhancing software engineering Through AI: A Comprehensive Exploration

Enhancing Software Engineering Through AI: A Comprehensive E...

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Computational Intelligence Approaches and Applications (ICCIAA), International Conference on

作者： Nedal Nwasra Ahmed Alsalemi Software Engineering Department University of Petra Amman Jordan Information Technology Department Al-Madinah College of Technology Madinah Saudi Arabia

ISBN: (数字)9798331523657

ISBN: (纸本)9798331523664

The integration of Artificial Intelligence (AI) techniques into software engineering (SE) has opened new avenues for improving software development, testing, and maintenance processes. This paper explores the transformative potential of AI-driven approaches in addressing some of the most persistent challenges in modern software engineering workflows. Specifically, it highlights the application of AI for automated bug detection and fixing in open-source software projects, which often grapple with complex codebases and evolving requirements. Through a detailed case study based on the Defects4J dataset, we demonstrate how a Transformer-based model can effectively detect bugs and suggest relevant fixes, showcasing an impressive 85% detection rate with a precision of 92%. Additionally, the model successfully proposed accurate fixes for 70% of identified bugs, as verified by developer feedback and performance testing. The integration of this model into Continuous Integration/Continuous Delivery (CI/CD) pipelines further reduced debugging time by 50%, underscoring its practical viability and substantial productivity gains. Moreover, developer feedback emphasized the system's usability, with many praising its ability to identify subtle bugs that often escape manual detection. However, challenges persist, including false positives and handling highly intricate bug scenarios, which remain areas for future refinement. This study not only underscores the efficacy of AI tools in augmenting traditional SE practices but also paves the way for future innovations in hybrid AI-human debugging workflows. By combining advanced machine learning architectures with real-world applicability, this research bridges the gap between theory and practice, offering a scalable solution for improving software reliability and developer efficiency.

关键词： Accuracy Computer bugs Pipelines Debugging Manuals Maintenance engineering Transformers Artificial intelligence software engineering Testing

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Optimizing Intrusion Detection with Hybrid Deep Learning Models and Data Balancing Techniques

Optimizing Intrusion Detection with Hybrid Deep Learning Mod...

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AI in Cybersecurity (ICAIC), International Conference on

作者： Md. Nasif Sarwar Md. Shohel Arman Touhid Bhuiyan Fatama Binta Rafiq Department of Software Engineering Daffodil International University Dhaka Bangladesh School of Information Technology Washington University of Science and Technology Virginia USA

ISBN: (数字)9798331518882

ISBN: (纸本)9798331518899

The effectiveness of hybrid deep learning models in detecting network intrusions on imbalanced datasets was tested. Conventional IDS often misses rare attacks due to class imbalance. Three models were evaluated: CNN+DNN, Autoencoder+DNN, and DNN+XGBoost, using the CSC IDS 2018 dataset balanced with SMOTE, FSMOTE, and CTGAN techniques. Feature selection was performed using SHAP and correlation studies. The models were evaluated on accuracy, precision, recall, F1-score, and ROC AUC. The Autoencoder+DNN and CNN+DNN models, especially with FSMOTE and SMOTE, showed the best performance. This research highlights the potential of combining advanced feature selection, data balancing, and deep learning techniques to enhance network intrusion detection, offering a robust framework for improved cybersecurity strategies.

关键词： Deep learning Correlation Accuracy Autoencoders Diversity reception Network intrusion detection Feature extraction Data models Computer security Artificial intelligence

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