This paper considers the security of non-minimum phase systems, a typical kind of cyber-physical systems. Non-minimum phase systems are characterized by unstable zeros in their transfer functions, making them particul...
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Diffusion models are a remarkably effective way of learning and sampling from a distribution p(x). In posterior sampling, one is also given a measurement model p(y | x) and a measurement y, and would like to sample fr...
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Diffusion models are a remarkably effective way of learning and sampling from a distribution p(x). In posterior sampling, one is also given a measurement model p(y | x) and a measurement y, and would like to sample from p(x | y). Posterior sampling is useful for tasks such as inpainting, super-resolution, and MRI reconstruction, so a number of recent works have given algorithms to heuristically approximate it;but none are known to converge to the correct distribution in polynomial time. In this paper we show that posterior sampling is computationally intractable: under the most basic assumption in cryptography-that one-way functions exist-there are instances for which every algorithm takes superpolynomial time, even though unconditional sampling is provably fast. We also show that the exponential-time rejection sampling algorithm is essentially optimal under the stronger plausible assumption that there are one-way functions that take exponential time to invert. Copyright 2024 by the author(s)
Brain tumor classification is essential for accurate diagnosis and treatment planning, significantly enhancing patient outcomes and survival rates. The complexity of multi-class classification, which includes Glioma, ...
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Sequence-to-sequence models are fundamental building blocks for generating abstractive text summaries, which can produce precise and coherent summaries. Recently proposed, different text summarization models aimed to ...
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Message Queuing Telemetry Transport (MQTT) has emerged as the widely adopted application layer protocol for IoT environments because of its lightweight header, minimal power, and bandwidth requirements. Despite its po...
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Message Queuing Telemetry Transport (MQTT) has emerged as the widely adopted application layer protocol for IoT environments because of its lightweight header, minimal power, and bandwidth requirements. Despite its popularity, the earlier version of the protocol, MQTT v3.1.1, encounters performance issues in large-scale implementations and required an update to handle the growing requirements of modern IoT applications. In response to these concerns, MQTT v5.0 was released with several significant features designed to enhance the reliability, user experience, and performance of IoT systems. While the MQTT protocol features were intended to facilitate robust and efficient communications, adversaries could exploit these features to mount various types of attacks in IoT deployments. More specifically, the Denial of Service (DoS) attacks towards the MQTT protocol have recently gained a lot of attention from the research community. However, the existing works primarily focus only on exploring the possibilities of misusing the MQTT v3.1.1 protocol features to generate DoS attacks in IoT realms. In this work, we attempt to extensively investigate the advanced protocol features of MQTT v5.0 that can be exploited to launch DDoS attacks impacting the IoT paradigm. We present the first critical evaluation of Distributed Denial of Service (DDoS) attacks on the MQTT v5.0 protocol by analyzing three significant features: CONNECT Properties, User Properties, and Flow Control. Moreover, we systematically propose attack scenarios based on the adversary's capabilities, thus illustrating the practicality of proposed attacks in real-world scenarios. Furthermore, we built a real-world testbed for IoT healthcare application to evaluate the severity of the identified attacks. The experimental results demonstrate the effectiveness of these attacks in impacting the availability of guaranteed IoT services to legitimate users, even in times of need. Additionally, we disclose the insightful fi
Domain adaptation is pivotal for enabling deep learning models to generalize across diverse domains, a task complicated by variations in presentation and cognitive nuances. In this paper, we introduce AD-Aligning, a n...
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A uniform triangular array (UTA) is proposed for physics-based 2D direction-of-arrival (DOA) estimations of unknown incoming signals. Three capacitively loaded top-hat antennas are used as array elements. Unlike conve...
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Ensuring data security and privacy in Internet of Things (IoT) is increasingly critical due to the growing interconnectedness of devices and the sensitivity of the data they handle. This paper presents a novel approac...
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The provision of rebate to needy/underprivileged sections of society has been in practice since long in government organizations. The efficacy of such provisions lies in the fact that whether this rebate reaches peopl...
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Ensuring secure and accurate node localization in Underwater Wireless Sensor Networks (UWSN) is a significant challenge, as conventional methods tend to neglect the security risks associated with malicious node interf...
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