Historians have tended to analyze maintenance as an intrinsically local activity, something very unlike the development of large technological systems. This article challenges this historiographic dichotomy by examining efforts to construct a global infrastructure for maintaining computer security. In the mid-1990s, as the internet rapidly grew, commercialized, and internationalized, a small community of computer security incident responders sought to scale up their system of coordination, which had been based on interpersonal trust, by developing trusted infrastructure that could facilitate the worldwide coordination of incident response work. This entailed developing not only professional standards, but also institutions for embodying and maintaining those standards in working infrastructure. While some elements of this infrastructure became truly global, others remained regionally bounded. We argue that this boundedness resulted not from the intrinsically local nature of maintenance, but from the historical process of infrastructure development, which was shaped by regionally based trust networks, institutions, and needs.
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http://dx.doi.org/10.1353/tech.2020.0036 | DOI Listing |
Enhancement of security, personalization, and safety in advanced transportation systems depends on driver identification. In this context, this work suggests a new method to find drivers by means of a Random Forest model optimized using the osprey optimization algorithm (OOA) for feature selection and the salp swarm optimization (SSO) for hyperparameter tuning based on driving behavior. The proposed model achieves an accuracy of 92%, a precision of 91%, a recall of 93%, and an F1-score of 92%, significantly outperforming traditional machine learning models such as XGBoost, CatBoost, and Support Vector Machines.
View Article and Find Full Text PDFNeural Netw
January 2025
Institute of High Performance Computing (IHPC), Agency for Science, Technology and Research (A*STAR), 138632, Singapore.
Pre-training and fine-tuning have become popular due to the rich representations embedded in large pre-trained models, which can be leveraged for downstream medical tasks. However, existing methods typically either fine-tune all parameters or only task-specific layers of pre-trained models, overlooking the variability in input medical images. As a result, these approaches may lack efficiency or effectiveness.
View Article and Find Full Text PDFMicrosc Res Tech
January 2025
AIDA Lab. College of Computer and Information Sciences (CCIS), Prince Sultan University, Riyadh, Saudi Arabia.
The development of deep learning algorithms has transformed medical image analysis, especially in brain tumor recognition. This research introduces a robust automatic microbrain tumor identification method utilizing the VGG16 deep learning model. Microscopy magnetic resonance imaging (MMRI) scans extract detailed features, providing multi-modal insights.
View Article and Find Full Text PDFSci Rep
January 2025
Computer Science Department, Faculty of Computers and Information, South Valley University, Qena, 83523, Egypt.
Adversarial attacks were commonly considered in computer vision (CV), but their effect on network security apps rests in the field of open investigation. As IoT, AI, and 5G endure to unite and understand the potential of Industry 4.0, security events and incidents on IoT systems have been enlarged.
View Article and Find Full Text PDFMethods
January 2025
School of Computer Science and Engineering, Central South University, Changsha 410083, China; Hunan Provincial Key Lab on Bioinformatics, Central South University, Changsha 410083, China.
Compound-protein interaction (CPI) prediction is critical in the early stages of drug discovery, narrowing the search space for CPIs and reducing the cost and time required for traditional high-throughput screening. However, CPI-related data are usually distributed across different institutions and their sharing is restricted because of data privacy and intellectual property rights. Constructing a scheme that enhances multi-institutional collaboration to improve prediction accuracy while protecting data privacy is essential.
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