Evolving neuro-fuzzy systems (ENFS) have shown great promise in analysis of streaming data, integrating artificial neural networks and fuzzy logic to create self-learning, data-driven models suitable for online (real-...
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The 3-phase task execution model has shown to be a good candidate to tackle the memory bus contention problem. It divides the execution of tasks into computation and memory phases that enable a fine-grained memory bus...
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Online Social network (OSN) is the most popular platform where users prefer to share images and videos. Image loading time in social media applications is time-consuming due to significantly less internet bandwidth. U...
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Despite its enormous economical and societal impact, lack of human-perceived control and safety is re-defining the design and development of emerging AI-based technologies. New regulatory requirements mandate increase...
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ISBN:
(纸本)9798350386066;9798350386059
Despite its enormous economical and societal impact, lack of human-perceived control and safety is re-defining the design and development of emerging AI-based technologies. New regulatory requirements mandate increased human control and oversight of AI, transforming the development practices and responsibilities of individuals interacting with AI. In this paper, we present the SPATIAL architecture, a system that augments modern applications with capabilities to gauge and monitor trustworthy properties of AI inference capabilities. To design SPATIAL, we first explore the evolution of modern system architectures and how AI components and pipelines are integrated. With this information, we then develop a proof-of-concept architecture that analyzes AI models in a human-in-the-loop manner. SPATIAL provides an AI dashboard for allowing individuals interacting with applications to obtain quantifiable insights about the AI decision process. This information is then used by human operators to comprehend possible issues that influence the performance of AI models and adjust or counter them. Through rigorous benchmarks and experiments in real-world industrial applications, we demonstrate that SPATIAL can easily augment modern applications with metrics to gauge and monitor trustworthiness, however, this in turn increases the complexity of developing and maintaining systems implementing AI. Our work highlights lessons learned and experiences from augmenting modern applications with mechanisms that support regulatory compliance of AI. In addition, we also present a road map of on-going challenges that require attention to achieve robust trustworthy analysis of AI and greater engagement of human oversight.
Integrated vehicle dynamics control systems require real-time communication among their components to improve performance and process efficiency. This communication relies on the use of sensor data, hardware interface...
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ISBN:
(纸本)9798350358810;9798350358803
Integrated vehicle dynamics control systems require real-time communication among their components to improve performance and process efficiency. This communication relies on the use of sensor data, hardware interfaces, transmission protocols, and control strategies, which all have an impact on the system's reliability. However, as the number of functionalized electronic control units (ECUs) and wiring systems increases, advanced control systems encounter complex functional and cybersecurity issues. To mitigate this complexity, the automotive industry widely employs the Controller Area Network (CAN) communication bus. Nevertheless, the inherent vulnerabilities of CAN and the rich interfaces with external environments increase the systems' susceptibility to soft errors caused by uncertainty factors such as process changes. Therefore, detecting abnormalities in automotive CAN communication is crucial. This paper introduces a machine learning (ML)-based anomaly detection framework to identify anomalies through CAN messages, extracting key features and employing ML models for predictive analysis. It also uses Triple Modular Redundancy (TMR) for trusted ML computation in anomaly detection. The study provides a comparative analysis of various ML algorithms, highlighting the effectiveness of Deep Neural Networks in identifying anomalies within both synthetic and real Hyundai CAN data for a wheel speed control system, showcasing the framework's capability to enhance system reliability and security.
Recently, ZNS SSDs have been actively researched to handle the functions of the FTL directly on the host system. ZNS SSDs can improve the I/O performance and spatial efficiency of SSDs by eliminating GC and eliminatin...
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ISBN:
(纸本)9798350339864
Recently, ZNS SSDs have been actively researched to handle the functions of the FTL directly on the host system. ZNS SSDs can improve the I/O performance and spatial efficiency of SSDs by eliminating GC and eliminating overprovisioning. In this paper, we analyze the performance by running distributed applications on a file system which supports ZNS SSDs. We found that much of the time difference occurs in the process of executing open and unlink operations rather than the performance of read and write operations. It has been confirmed that the performance difference of read/write operations is not significant for the applications. If structural optimization is made on file system, it has been found that ZNS SSDs are better than CNS SSDs of the same capacity in terms of price.
In the rapidly evolving digital era, we have witnessed the rise of computation-intensive and transmission-intensive applications like multi-party real-time video communication, remote medical surgeries, and online edu...
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Monitoring and tracing are integral to embeddedsystems development. In context of real-timesystems, overhead is of essence as the timing behavior might be affected. In this paper we present real-time Monitor and Tra...
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A new innovation called wearable assistive robotics has the potential of assisting those with sensorimotor disabilities in doing routine tasks. A lot of research is done on soft robots because of its adaptability, def...
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Sign language is the primary mode of communication for deaf and hard of hearing community. This paper discusses the development of a realtime sign language recognition system by deploying deep learning techniques. Fo...
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