This article presents a data interface for maritime simulators to facilitate the development of ship Automatic Navigational systems (ANS) at different autonomy levels. The core architecture of this data interface cons...
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The integration of healthcare data storage and management within cloud systems has revolutionized accessibility and administration in the medical field. This paper, proposes a security framework for medical data over ...
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Training of Machine Learning (ML) models requires huge amounts of data. Usually, in the training of sophisticated models, data-sets can be very computationally intensive on resources. In order to reduce the computatio...
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The emergence of many-core processors presents significant opportunities for large-scale multithreading. Exploiting these intensive computing resources poses an urgent challenge for data processing systems. Although c...
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Terminal devices are critical in establishing efficient, reliable, and secure computer network systems. Anomaly detection, a key technique in computer security, is widely researched for its adaptability and ability to...
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ISBN:
(纸本)9798350350920
Terminal devices are critical in establishing efficient, reliable, and secure computer network systems. Anomaly detection, a key technique in computer security, is widely researched for its adaptability and ability to recognize novel attacks, despite facing issues such as low efficiency. To address the monitoring and identification of anomalous behaviors by users during data access at the terminal level, a comprehensive assessment incorporating data content, methods of operation, and user behavior is essential. Therefore, a terminal-level data access anomaly detection system utilizing the TL-IDA algorithm is proposed. This system employs a sliding window technique to enhance its performance. The primary aim is to increase data security and prevent unauthorized access, providing guidance for the prompt adoption of appropriate security measures to ensure the protection of data.
This article studies the data-driven design of parity space based replay attack detection method for cyber-physical systems. By utilizing the input and output data, the proposed data-driven detection can uncover steal...
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We address the challenges of the semi-supervised LiDAR segmentation (SSLS) problem, particularly in low-budget scenarios. The two main issues in low-budget SSLS are the poor-quality pseudo-labels for unlabeled data, a...
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ISBN:
(纸本)9798350377712;9798350377705
We address the challenges of the semi-supervised LiDAR segmentation (SSLS) problem, particularly in low-budget scenarios. The two main issues in low-budget SSLS are the poor-quality pseudo-labels for unlabeled data, and the performance drops due to the significant imbalance between ground-truth and pseudo-labels. This imbalance leads to a vicious training cycle. To overcome these challenges, we leverage the spatio-temporal prior by recognizing the substantial overlap between temporally adjacent LiDAR scans. We propose a proximity-based label estimation, which generates highly accurate pseudo-labels for unlabeled data by utilizing semantic consistency with adjacent labeled data. Additionally, we enhance this method by progressively expanding the pseudo-labels from the nearest unlabeled scans, which helps significantly reduce errors linked to dynamic classes. Additionally, we employ a dual-branch structure to mitigate performance degradation caused by data imbalance. Experimental results demonstrate remarkable performance in low-budget settings (i.e., <= 5%) and meaningful improvements in normal budget settings (i.e., 5 - 50%). Finally, our method has achieved new state-of-the-art results on SemanticKITTI and nuScenes in semi-supervised LiDAR segmentation. With only 5% labeled data, it offers competitive results against fully-supervised counterparts. Moreover, it surpasses the performance of the previous state-of-the-art at 100% labeled data (75.2%) using only 20% of labeled data (76.0%) on nuScenes. The code is available on https://***/halbielee/PLE.
With the acceleration of the digital transformation process of the power grid, various business systems of the power grid generate diverse types of data, inconsistent data formats, and serious data silos. In order to ...
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ISBN:
(纸本)9798350375145;9798350375138
With the acceleration of the digital transformation process of the power grid, various business systems of the power grid generate diverse types of data, inconsistent data formats, and serious data silos. In order to solve the problem of data interoperability, improve data sharing and application efficiency, this article designs a middleware solution that can customize data format conversion and data integration for various business data in the power grid. The underlying layer of this middleware uses a specially designed rule parent table data structure, and combines the GORM framework and GEN tool in development, which not only reduces code redundancy but also improves program scalability, providing a new approach for work similar to business requirements.
The process of deciphering, extracting, and compiling information from texts dense with domain-specific terminology and technical jargon is a challenging endeavor. It demands considerable expertise and deep knowledge ...
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This paper describes a fast and reliable technique for communication between two short-range devices over wired or wireless networks, using a self-designed transmitter and receiver system modules. A good and lossless ...
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