Today, online reviews have a great influence on consumers’ purchasing decisions. As a result, spam attacks, consisting of the malicious inclusion of fake online reviews, can be detrimental to both customers as well a...
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This paper introduces an Access Gate Function (AGF) that has been implemented using the P4 language and evaluates its performance when running on a Tofino switch. Through the process of translating specifications from...
This paper introduces an Access Gate Function (AGF) that has been implemented using the P4 language and evaluates its performance when running on a Tofino switch. Through the process of translating specifications from the Broadband Forum (BBF) for Fixed Mobile Convergence in the 5G Core into a P4 code and by conducting data plane simulations on software switch models, we provide evidence that the AGF solution conforms to industry standards and exhibits efficient and scalable performance characteristics. Subsequently, we assess the prototype’s implementation on a Tofino switch, analyzing its capabilities and limitations. The reported results demonstrate that the programmable P4 Tofino switch efficiently handles a significant number of sessions, further reinforcing the potential and practicality of the proposed solution for industrial deployment in fixed-mobile convergence scenarios.
Collaborative Simultaneous Localization And Mapping (C-SLAM) is a vital component for successful multirobot operations in environments without an external positioning system, such as indoors, underground or underwater...
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Zero-knowledge proof systems are becoming powerful tools in domains like blockchain networks. In this work, we improve abstract interpretation methods to sanity check PLONKish arithmetizations of computation, such as ...
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ISBN:
(数字)9798331504830
ISBN:
(纸本)9798331515621
Zero-knowledge proof systems are becoming powerful tools in domains like blockchain networks. In this work, we improve abstract interpretation methods to sanity check PLONKish arithmetizations of computation, such as those used in the Halo2 zero-knowledge proof system. Our work aims to improve the accuracy of checks for unused gates, assigned but unconstrained values, and possibly under-constrained circuits, all of which may indicate a bug in the circuits. We show how copy constraints and arbitrary lookup functions can be used in abstract interpretation based analysis of these circuits. We are motivated to revisit this topic as these circuits, through their use in general purpose blockchain networks, may be responsible for the security of millions of dollars in cryptocurrency.
In the field of radiocommunication, modulation type identification is one of the most important characteristics in signal processing. This study aims to implement a modulation recognition system on two approaches to m...
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In the field of radiocommunication, modulation type identification is one of the most important characteristics in signal processing. This study aims to implement a modulation recognition system on two approaches to machine learning techniques, the K-Nearest Neighbors (KNN) and Artificial Neural Networks (ANN). From a statistical and spectral analysis of signals, nine key differentiation features are extracted and used as input vectors for each trained model. The feature extraction is performed by using the Hilbert transform, the forward and inverse Fourier transforms. The experiments with the AMC Master dataset classify ten (10) types of analog and digital modulations. AM_DSB_FC, AM_DSB_SC, AM_USB, AM_LSB, FM, MPSK, 2PSK, MASK, 2ASK, MQAM are put forward in this article. For the simulation of the chosen model, signals are polluted by the Additive White Gaussian Noise (AWGN). The simulation results show that the best identification rate is the MLP neuronal method with 90.5% of accuracy after 10 dB signal-to-noise ratio value, with a shift of more than 15% from the k-nearest neighbors’ algorithm.
- This paper presents an instrument for automated measurement of temperature dependence of voltampere and lumen-ampere characteristics of LEDs. It consists of a programmable current source, temperature control module,...
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Model stealing attacks have become a serious concern for deep learning models, where an attacker can steal a trained model by querying its black-box API. This can lead to intellectual property theft and other security...
Model stealing attacks have become a serious concern for deep learning models, where an attacker can steal a trained model by querying its black-box API. This can lead to intellectual property theft and other security and privacy risks. The current state-of-the-art defenses against model stealing attacks suggest adding perturbations to the prediction probabilities. However, they suffer from heavy computations and make impracticable assumptions about the adversary. They often require the training of auxiliary models. This can be time-consuming and resource-intensive which hinders the deployment of these defenses in real-world applications. In this paper, we propose a simple yet effective and efficient defense alternative. We introduce a heuristic approach to perturb the output probabilities. The proposed defense can be easily integrated into models without additional training. We show that our defense is effective in defending against three state-of-the-art stealing attacks. We evaluate our approach on large and quantized (i.e., compressed) Convolutional Neural Networks (CNNs) trained on several vision datasets. Our technique outperforms the state-of-the-art defenses with a ×37 faster inference latency without requiring any additional model and with a low impact on the model's performance. We validate that our defense is also effective for quantized CNNs targeting edge devices.
Membrane proteins make up around 30% of all proteins in a cell. These proteins are difficult to evaluate due to their hydrophobic surface and dependence on their original in vivo environment. There is a tremendous dem...
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Model stealing attacks have become a serious concern for deep learning models, where an attacker can steal a trained model by querying its black-box API. This can lead to intellectual property theft and other security...
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Image-based 3D reconstruction is one of the most important tasks in computer Vision with many solutions proposed over the last few decades. The objective is to extract metric information i.e. the geometry of scene obj...
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