Rapid, nondestructive and accurate detection of internal qualities of the apple is an important research interest. In this study, the brix, acidity and brix/acidity ratio of the apple were rapidly detected by visible and near-infrared spectroscopy (VIS-NIRS). By scanning spectra and measuring the reference values of brix and acidity of apple samples, the relationship models between the spectra and brix, acidity, brix/acidity ratio were, respectively, established. Sample division, characteristic wavelength optimization, and modeling methods were compared systematically, and the optimal prediction model of each quality index was determined. The experimental results show that the competitive adaptive reweighted sampling method can effectively select characteristic wavelengths, which not only improves the prediction speed, but also greatly enhances the prediction accuracy. The established partial least squares models based on these selected characteristic wavelengths all have high accuracy and robustness for the three quality indices. The determination coefficients of the models are 0.9899, 0.9615, 0.9535, and the relative percent deviation are 9.9269, 5.0987, 4.6374, respectively. All this work proves that VIS-NIRS can be used for rapid and nondestructive detection of the internal qualities of an apple.

Selecting the decisive characteristic variables is particularly important to analyze the soluble solids content (SSC) of an apple with visible/near-infrared spectroscopy (VIS-NIRS) technology. The multi-population genetic algorithm (MPGA) was applied to variable selection for the first time, to the best of our knowledge. A hybrid variable selection method combined competitive adaptive reweighted sampling (CARS) with MPGA (CARS-MPGA) was proposed. In this method, CARS was firstly used to shrink the variable space, and then the MPGA was used to further fine select the characteristic variables. Based on CARS-MPGA, a nondestructive quantitative detection SSC model of an apple was established and compared with the models established by different variable selection methods, such as successive projections algorithm, synergy interval partial least squares, and genetic algorithm. The experiments showed that the CARS-MPGA model was the best. The number of modeling variables was only 64, and the determination coefficients, root mean squared error, and residual predictive deviation for the prediction set were 0.853, 0.443, and 2.612, respectively. The results demonstrated that the CARS-MPGA is a reliable variable selection method and can be used for fast nondestructive detection SSC of an apple.

API call sequence-based approaches are proven to have significant superiority in malware detection but generally overlook or evade two core issues: ( i ) ignoring parameters and return values that contain more fine-grained security semantic sensitive information (SSSI) and ( ii ) handling lengthy API call sequences roughly, causing the poor interpretability and incompleteness of program behavior semantics. To effectively overcome these issues, we propose SIa-CBc, a sensitive intent-assisted and crucial behavior-cognized malware detection method leveraging human brain cognitive theory, which consists of two key modules. ( i ) SIa divides the vast and heterogeneous SSSI space into a few categories, meanwhile representing the sensitive intents to assist API calls. ( ii ) CBc extracts crucial snippets from lengthy API call sequences via judgment and multi-step reasoning and further obtains their representations. The embedding representations from the previous two modules are concatenated as the input of ten representative baseline networks. Our experimental results indicate that SIa-CBc achieves an enhancement in malware detection accuracy ranging from 14.08% to 28.01%, reduces the average detection time per sample by 0.28 to 16.29 ms, and improves the defense against adversarial sample attacks by 4.86% to 55.04%. Moreover, SIa-CBc demonstrates outstanding performance compared to recent methods, not only limited to detection but also encompassing enhanced resilience to intricate adversarial tactics, thereby ensuring reliable protection without the need for frequent re-training. This underscores the model’s innovative approach in leveraging human brain cognitive theory-based techniques for heightened security efficacy.

Cache side channel attacks, as a type of cryptanalysis, seriously threaten the security of the cryptosystem. These attacks continuously monitor the memory addresses associated with the victim’s secret information, which cause frequent memory access on these addresses. This paper proposes CacheHawkeye, which uses the frequent memory access characteristic of the attacker to detect attacks. CacheHawkeye monitors memory events by CPU hardware performance counters. We proved the effectiveness of CacheHawkeye on Flush+Reload and Flush+Flush attacks. In addition, we evaluated the accuracy of CacheHawkeye under different system loads. Experiments demonstrate that CacheHawkeye not only has good accuracy but can also adapt to various system loads.