The physical unclonable functions(PUFs) are novel cryptographic primitives in modern hardware security *** with traditional alternatives based on digital keys and non-volatile memory(NVM),the PUF system shows great un...
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The physical unclonable functions(PUFs) are novel cryptographic primitives in modern hardware security *** with traditional alternatives based on digital keys and non-volatile memory(NVM),the PUF system shows great unclonability,high efficiency,and physical attack ***,the conventional PUF design suffers from weak machine learning immunity,high storage overhead,and unreliability,making it difficult to implement in the Internet of Things(Io T) and edge computing *** paper presents a new PUF design that could solve the proposed *** utilizing the emission probability of traps commonly found in nano-scaled transistors,a model-based PUF system with strong machine learning resistance could be *** PUF design,called Prob-PUF,needs fewer challengeresponse pairs(CRPs) space and reveals superior resistance to modeling attacks due to the mixture of stable/random bits in its output ***,the Prob-PUF system could reach a high level of uniqueness and robustness,making it a potential candidate for future cryptographically secured protocols within the IoT.
Cu2ZnSn(S,Se)4 (CZTSSe), despite its slow progress in terms of efficiency, stands out as a viable option for solar cell absorber materials due to its comparatively low-cost fabrication process, and abundance of consti...
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Despite notable progress in enhancing the capability of machine learning against distribution shifts, training data quality remains a bottleneck for cross-distribution generalization. Recently, from a data-centric per...
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Despite notable progress in enhancing the capability of machine learning against distribution shifts, training data quality remains a bottleneck for cross-distribution generalization. Recently, from a data-centric perspective, there have been considerable efforts to improve model performance through refining the preparation of training data. Inspired by realistic scenarios, this paper addresses a practical requirement of acquiring training samples from various domains on a limited budget to facilitate model generalization to target test domain with distribution shift. Our empirical evidence indicates that the advance in data acquisition can significantly benefit the model performance on shifted data. Additionally, by leveraging unlabeled test domain data, we introduce a Domain-wise Active Acquisition framework. This framework iteratively optimizes the data acquisition strategy as training samples are accumulated, theoretically ensuring the effective approximation of test distribution. Extensive real-world experiments demonstrate our proposal's advantages in machine learning applications. The code is available at https://***/dongbaili/DAA. Copyright 2024 by the author(s)
Speaker extraction to separate the target speech from the mixed audio is a problem worth studying in the speech separation field. Since human pronunciation is closely related to lip motions and facial expressions duri...
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For permanent magnet synchronous machines(PMSMs),accurate inductance is critical for control design and condition *** to magnetic saturation,existing methods require nonlinear saturation model and measurements from mu...
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For permanent magnet synchronous machines(PMSMs),accurate inductance is critical for control design and condition *** to magnetic saturation,existing methods require nonlinear saturation model and measurements from multiple load/current conditions,and the estimation is relying on the accuracy of saturation model and other machine parameters in the *** harmonic produced by harmonic currents is inductance-dependent,and thus this paper explores the use of magnitude and phase angle of the speed harmonic for accurate inductance *** estimation models are built based on either the magnitude or phase angle,and the inductances can be from d-axis voltage and the magnitude or phase angle,in which the filter influence in harmonic extraction is considered to ensure the estimation *** inductances can be estimated from the measurements under one load condition,which is free of saturation ***,the inductance estimation is robust to the change of other machine *** proposed approach can effectively improve estimation accuracy especially under the condition with low current *** and comparisons are conducted on a test PMSM to validate the proposed approach.
With the rapid advancement of speech recognition and semantic understanding, speech interaction systems have gained widespread usage. This paper investigates the use of intelligent error handling approaches in speech ...
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Current semi-supervised learning-based sample selection methods for noisy label image classification typically utilize all clean and noisy samples for model training. However, not all noisy samples contribute positive...
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The exponential growth of big data has led to the widespread adoption of Hadoop clusters for storing and processing large volumes of data. Efficient management of resources within these clusters is crucial for achievi...
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Stochastic compositional optimization (SCO) problems are popular in many real-world applications, including risk management, reinforcement learning, and meta-learning. However, most of the previous methods for SCO req...
To better understand complex human emotions, there is growing interest in utilizing heterogeneous sensory data to detect multiple co-occurring emotions. However, existing studies have focused on extracting static info...
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