Non-malleable code is an encoding scheme that is useful in situations where traditional error correction or detection is impossible to *** ensures with high probability that decoded message is either completely unrela...
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Non-malleable code is an encoding scheme that is useful in situations where traditional error correction or detection is impossible to *** ensures with high probability that decoded message is either completely unrelated or the original one,when tampering has no ***,standard version of non-malleable codes provide security against one time tampering *** ciphers are successfully employed in the construction of non-malleable *** construction fails to provide security when an adversary tampers the codeword more than *** non-malleable codes further allow an attacker to tamper the message for polynomial number of *** this work,we propose continuous version of non-malleable codes from block ciphers in split-state *** construction provides security against polynomial number of tampering attacks and it preserves *** the tam-pering experiment triggers self-destruct,the security of continuously non-malleable code reduces to security of the underlying leakage resilient storage.
The missing readings in various sensors of air pollution monitoring stations is a common issue. Those missing sensor readings may greatly influence the performance of monitoring and analysis of air pollution data. To ...
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Medical imaging, a cornerstone of disease diagnosis and treatment planning, faces the hurdles of subjective interpretation and reliance on specialized expertise. Deep learning algorithms show improvements in automatin...
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In this paper,we innovatively associate the mutual information with the frame error rate(FER)performance and propose novel quantized decoders for polar *** on the optimal quantizer of binary-input discrete memoryless ...
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In this paper,we innovatively associate the mutual information with the frame error rate(FER)performance and propose novel quantized decoders for polar *** on the optimal quantizer of binary-input discrete memoryless channels(BDMCs),the proposed decoders quantize the virtual subchannels of polar codes to maximize mutual information(MMI)between source bits and quantized *** nested structure of polar codes ensures that the MMI quantization can be implemented stage by *** results show that the proposed MMI decoders with 4 quantization bits outperform the existing nonuniform quantized decoders that minimize mean-squared error(MMSE)with 4 quantization bits,and yield even better performance than uniform MMI quantized decoders with 5 quantization ***,the proposed 5-bit quantized MMI decoders approach the floating-point decoders with negligible performance loss.
We studied the weekly number and the growth/decline rates of COVID-19 deaths of the period from October 31, 2022, to February 9, 2023, in Italy. We found that the COVID-19 winter wave reached its peak during the three...
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Multi-document summarising (MDS) is a helpful method for information aggregation that creates a clear and informative summary from a collection of papers linked to the same subject. Due to the significant number of in...
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A cyber range provides a controlled environment for simulating mission-critical systems for cybersecurity research following the high risks involved in conducting experiments in real-life systems. Existing cyber range...
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Ecological validity remains essential for generalizing scientific research into real-world applications. However, current methods for crowd emotion detection lack ecological validity due to limited diversity samples i...
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Deep neural networks (DNNs) are crucial in autonomous driving systems (ADSs) for tasks like steering control, but model inaccuracies, biased training data, and incorrect runtime parameters can compromise their reliabi...
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Deep neural networks (DNNs) are crucial in autonomous driving systems (ADSs) for tasks like steering control, but model inaccuracies, biased training data, and incorrect runtime parameters can compromise their reliability. Metamorphic testing (MT) enhances reliability by generating follow-up tests from mutated DNN source inputs, identifying inconsistencies as defects. Various MT techniques for ADSs include generative/transfer models, neuron-based coverage maximization, and adaptive test selection. Despite these efforts, significant challenges remain, including the ambiguity of neuron coverage’s correlation with misbehaviour detection, a lack of focus on DNN critical pathways, inadequate use of search-based methods, and the absence of an integrated method that effectively selects sources and generates follow-ups. This paper addresses such challenges by introducing DeepDomain, a grey-box multi-objective test generation approach for DNN models. It involves adaptively selecting diverse source inputs and generating domain-oriented follow-up tests. Such follow-ups explore critical pathways, extracted by neuron contribution, with broader coverage compared to their source tests (inter-behavioural domain) and attaining high neural boundary coverage of the misbehaviour regions detected in previous follow-ups (intra-behavioural domain). An empirical evaluation of the proposed approach on three DNN models used in the Udacity self-driving car challenge, and 18 different MRs demonstrates that relying on behavioural domain adequacy is a more reliable indicator than coverage criteria for effectively guiding the testing of DNNs. Additionally, DeepDomain significantly outperforms selected baselines in misbehaviour detection by up to 94 times, fault-revealing capability by up to 79%, output diversity by 71%, corner-case detection by up to 187 times, identification of robustness subdomains of MRs by up to 33 percentage points, and naturalness by two times. The results confirm that stat
Images are used widely nowadays. Images are used in many fields such as medicine to terrain mapping. There is a need to compress the images and represent them in shorter form for effective transmission. Several techni...
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