Out-of-distribution (OOD) detectors can act as safety monitors in embedded cyber-physical systems by identifying samples outside a machine learning model’s training distribution to prevent potentially unsafe actions....
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The connectivity requirements brought by Industry 4.0 pose new challenges for cyber-physical systems (CPS), such as the strengthening of their resilience against cyberattacks. In this paper, we use the discrete event ...
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The connectivity requirements brought by Industry 4.0 pose new challenges for cyber-physical systems (CPS), such as the strengthening of their resilience against cyberattacks. In this paper, we use the discrete event systems (DES) formalism to develop an approach that aims to protect CPS from covert actuator attacks using event permutation maps. In this approach, properly selected controllable events are permuted on the plant's site so that attacks on actuators do not have the impact on the plant as expected by the intruder and unsafe states are avoided. Furthermore, a property termed AE-protectability is presented to characterize the necessary and sufficient conditions for the system to be protected against such attacks through event permutations. The proposed approach is applied to a case study where the Identification of the attack is done without causing damage while maintaining the integrity of the system.
Processing and storing the 4D structure of light fields can be challenging and expensive due to the high-dimensional data and its unique characteristics. There are plenty of works employing convolutional neural networ...
Processing and storing the 4D structure of light fields can be challenging and expensive due to the high-dimensional data and its unique characteristics. There are plenty of works employing convolutional neural networks (CNNs) for light field prediction and encoding. Nonetheless, to the best of our knowledge, the literature lacks an efficiency evaluation of different CNN architectures as well as 4D neural networks for these purposes. Therefore, this paper presents an experimental study that assesses the performance of pipeline and U-net convolutional neural networks for light field block prediction in both spatial and angular dimensions. Additionally, we compare these architectures with a novel 4D network that aims to exploit the light field data structure. The results of the study show that U-net and 4D networks outperform classical CNN architectures in terms of prediction accuracy and residue generation. Furthermore, the spatial dimension prediction provides more valuable information for the networks to learn, improving their prediction by 5dB.
The popularization of mobile phones and other multimedia portable devices paved the way for the increase in video consumption worldwide. However, it is impossible to transmit a non-compressed video due to the high ban...
The popularization of mobile phones and other multimedia portable devices paved the way for the increase in video consumption worldwide. However, it is impossible to transmit a non-compressed video due to the high bandwidth required. To achieve significant compression rates, video codecs usually employ methods that damage the visual quality perceived by the end user in non-negligible levels. Different architectures based on deep learning have been recently proposed for Video Quality Enhancement (VQE). Still, most of them are trained and validated using videos generated by a single codec under fixed configurations. With the increase of video coding formats and standards on the market, VQE methods that apply to different contexts are desired. This paper proposes a new VQE model based on the Spatio- Temporal Deformable Fusion (STDF) archi-tecture, providing quality gains for videos compressed according to different formats and standards, such as HEVC, VVC, VP9, and AVI. The results demonstrate that by considering different video coding standards and formats to build the STDF model, a significant increase in VQE is achieved, with an average PSNR increment of up to 0.382 dB.
Benefits of a failure friendly culture, e.g., learning from failure, are widely known in occupational settings. Validated scales have been developed to measure organizational failure culture and individuals' mind-...
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Penile cancer, although rare, has an increasing mortality rate in Brazil, highlighting the need for effective diagnostic methods. Artificial Intelligence (AI) in histopathological analysis can speed up and objectify d...
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In-band network telemetry is a powerful framework for network monitoring. It allows the collection of telemetry data in real-time and provides network-wide visibility. However, depending on the routing of network flow...
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In-band network telemetry is a powerful framework for network monitoring. It allows the collection of telemetry data in real-time and provides network-wide visibility. However, depending on the routing of network flows and which telemetry data are collected, the network-wide visibility and the performance of monitoring applications may decrease. In this paper, we present the in-band network telemetry problem and extend the existing mathematical optimization models of the problem by proposing a new model that computes the routing of network flows. Results show that the new model outperforms existing models in term of network coverage and monitoring applications performance. The results of this work can be useful for network managers and enterprises to gain real-time insights into network performance.
The quality of communication with a computer impacts how the designer performs during the design process. Today, Artificial Intelligence (AI) empowers the designer by expanding the solution space using the expertise f...
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Generative systems are becoming a crucial part of current design practice. There exist gaps however, between the digital processes, field data and designer's input. To solve this problem, multiple processes were d...
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This work presents an optimized exponential function VLSI hardware design by Taylor series expansion. The proposed architecture implements the exponential by approximating the logic design of a 4 th -order Taylor seri...
This work presents an optimized exponential function VLSI hardware design by Taylor series expansion. The proposed architecture implements the exponential by approximating the logic design of a 4 th -order Taylor series and explores efficient CMOS arithmetic operation strategies. It implements a shift-based divider and explores an efficient 4-2 adder compressor in the adder tree. The proposal with a −7 to 11 input values range shows an output error of around 2% of MRED with a reduced energy consumption of 3.63 pJ/operation for 32-bit output. For a 64-bit output, the energy per operation of the VLSI exponential unit is 14.97pJ/op, being able to process a more comprehensive input range (i.e., −14 to 22) for a negligible mean output error of around 1.7% of MRED.
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