Robin: A Novel Method to Produce Robust Interpreters for Deep Learning-Based Code Classifiers

Deep learning has been widely used in source code classification tasks, such as code classification according to their functionalities, code authorship attribution, and vulnerability detection. Unfortunately, the black-box nature of deep learning makes it hard to interpret and understand why a classifier (i.e., classification model) makes a particular prediction on a given example. This lack of interpretability (or explainability) might have hindered their adoption by practitioners because it is not clear when they should or should not trust a classifier's prediction. The lack of interpretability has motivated a number of studies in recent years. However, existing methods are neither robust nor able to cope with out-of-distribution examples. In this paper, we propose a novel method to produce \underline{Rob}ust \underline{in}terpreters for a given deep learning-based code classifier; the method is dubbed Robin. The key idea behind Robin is a novel hybrid structure combining an interpreter and two approximators, while leveraging the ideas of adversarial training and data augmentation. Experimental results show that on average the interpreter produced by Robin achieves a 6.11\% higher fidelity (evaluated on the classifier), 67.22\% higher fidelity (evaluated on the approximator), and 15.87x higher robustness than that of the three existing interpreters we evaluated. Moreover, the interpreter is 47.31\% less affected by out-of-distribution examples than that of LEMNA.Comment: To be published in the 38th IEEE/ACM International Conference on Automated Software Engineering (ASE 2023

On the Effectiveness of Function-Level Vulnerability Detectors for Inter-Procedural Vulnerabilities

Software vulnerabilities are a major cyber threat and it is important to detect them. One important approach to detecting vulnerabilities is to use deep learning while treating a program function as a whole, known as function-level vulnerability detectors. However, the limitation of this approach is not understood. In this paper, we investigate its limitation in detecting one class of vulnerabilities known as inter-procedural vulnerabilities, where the to-be-patched statements and the vulnerability-triggering statements belong to different functions. For this purpose, we create the first Inter-Procedural Vulnerability Dataset (InterPVD) based on C/C++ open-source software, and we propose a tool dubbed VulTrigger for identifying vulnerability-triggering statements across functions. Experimental results show that VulTrigger can effectively identify vulnerability-triggering statements and inter-procedural vulnerabilities. Our findings include: (i) inter-procedural vulnerabilities are prevalent with an average of 2.8 inter-procedural layers; and (ii) function-level vulnerability detectors are much less effective in detecting to-be-patched functions of inter-procedural vulnerabilities than detecting their counterparts of intra-procedural vulnerabilities.Comment: 12 pages, 7 figures. To appear in the Proceedings of the 46th International Conference on Software Engineering (ICSE'24

Analysis of Odor Components in Salted Egg Yolk during Thermal Processing Using Gas Chromatography-Mass Spectrometry and Multivariate Statistical Analysis

Odor compounds produced in salted egg yolk during thermal processing were identify by sensory evaluation and gas chromatography-mass spectrometry (GC-MS) combined with multivariate statistical analysis such as hierarchical cluster analysis (HCA), orthogonal partial least squares-discriminant analysis (OPLS-DA) and principal component analysis (PCA). By using quantitative descriptive sensory analysis, it was found that there was a significant difference in the flavor profile between egg yolk samples with and without unpleasant odor, and it was determined that some egg yolks had obvious off-flavor during hot processing. Cluster analysis showed that the six groups of salted egg yolk samples were clearly divided into two categories, which was consistent with the sensory evaluation results. Furthermore, OPLS-DA identified 33 major differential components with variable importance in the projection (VIP) scores greater than one between samples with and without unpleasant odor, and aldehydes and alcohols were the most significant differential components. Finally, the volatile compounds showing a positive correlation with sour odor were screened by PCA, and n-hexanal, n-valeraldehyde, 1-octen-3-ol, trans,trans-2,4-decadienal, styrene, dimethyl disulfide and 2-nonone were identified due to their odor activity values (OAV) greater than one as the major odor compounds of salted egg yolk during thermal processing. The results provide a reference for analysis of the flavor characteristics and possible off-flavor components of salted egg yolk after thermal processing and provide a scientific basis for accessing the quality of salted egg yolk

Achieving ultrahigh energy storage density in super relaxor BCZT-based lead-free capacitors through multiphase coexistence

Dielectric capacitors own great potential in next-generation energy storage devices for their fast charge-discharge time, while low energy storage capacity limits their commercialization. Enormous lead-free ferroelectric ceramic capacitor systems have been reported in recent decades, and energy storage density has increased rapidly. By comparing with some ceramic systems with fashioned materials or techniques, which lacks repeatability, as reported latterly, we proposed a unique but straightforward way to boost the energy storage capacity in a modified conventional ferroelectric system. Through stoichiometric ratio regulation, the coexistence of the C-phase and T-phase was obtained in 0.85(Ba1-xCax)(ZryTi1-y)O3-0.15BiSmO3-2 wt. % MnO ceramics with x = 0.1 and y = 0.15 under the proof of the combination of Rietveld XRD refinement and transmission electron microscope measurement. The Wrec of 3.90 J/cm3, an excellent value for BCZT-based ceramic at the present stage, was obtained because of the co-contribution of the optimization of electric field distribution and the additional interfacial polarization triggered at the higher electric fields. The finite element simulation and physical deduction, which fits very well with our experimental result, were also performed. As to the practical application, stable performance in a long-time cycle and frequency stability was obtained, and excellent discharge behaviors were also achieved.</p

Effects of Different Selenium Enrichment Methods on the Antioxidant Activity of Sweet Potato Leaf Proteins

This study investigated the effect of foliar versus soil application of selenium on the selenium content of sweet potato leaves and the antioxidant activity of sweet potato leaf proteins. The results showed that both foliar and soil applications of selenium could increase the protein-bound selenium content of sweet potato leaves. The protein-bound selenium contents of the foliar and soil selenium treatment groups at 1.6 mg/mL concentration were 20.10 and 1.68 mg/g, respectively. Selenium enrichment could improve the antioxidant activity of sweet potato leaf proteins. The superoxide anion free radical scavenging capacity of sweet potato leaf proteins was increased by 19.17% with foliar selenium enrichment. According to correlation analysis, the total selenium content and protein-bound selenium content of sweet potato leaves significantly affected the antioxidant activity, and the total phenolic and total flavonoid contents also affected the antioxidant activity. In conclusion, compared with soil selenium application, foliar selenium application was better in increasing the protein-bound selenium content of sweet potato leaves

Sex Differences in the Association of HOMA-IR Index and BDNF in Han Chinese Patients With Chronic Schizophrenia

Background: Previous research has indicated that there are significant sex differences in serum BDNF levels and metabolic indicators in patients with schizophrenia. Studies have found that BDNF is involved in blood sugar regulation. Homeostasis model assessment of insulin resistance (HOMA-IR) is currently a sensitive indicator for measuring insulin resistance. Our study aims to explore the sex differences in the relationship between serum BDNF levels and HOMA-IR in patients with chronic schizophrenia (CS).Methods: A total of 332 patients with CS were enrolled in this study. General information of all participants was collected. Haematological indicators were collected, and the Positive and Negative Syndrome Scale (PANSS) was used to evaluate psychiatric symptoms. Sex differences in serum BDNF levels, HOMA-IR index and other metabolic indexes were investigated. Then, linear regression analysis was used to analyse the relationship between the HOMA-IR index and BDNF levels in male and female patients.Results: The HOMA-IR index of female patients was significantly higher than that of males, but there was no significant difference in serum BDNF levels between male patients and female patients. There was a positive correlation between BDNF level and HOMA-IR index, and this relationship only existed in female patients.Conclusion: The results show that there are significant sex differences in HOMA-IR in patients with CS. In addition, only in female patients was there a positive correlation between the HOMA-IR index and BDNF level, which suggests that sex factors should be taken into account in evaluating the relationship between BDNF and blood glucose in patients with CS

Angiotensin II induces the secretion of ICAM-1 and MCP-1 in human airway smooth muscle cells in vitro

Background: Angiotensin [1] II is known to cause human airway smooth muscle (HASM) contraction and closely relate to asthma, however, the effect of Ang II on HASM cells (HASMCs) secretion function is still unclear. Methods: Primary cultured HASMCs were treated with Ang II, Ang II + Ang-(1–7), and Ang II + irbersatan (IRB), respectively. The nuclear translocation of the NF-κB p65 in HASMCs in each group was analyzed performed with Immunofluorescence. The expression levels of intercellular adhesion molecule-1 (ICAM-1) and monocyte chemoattractant protein-1 (MCP-1) were evaluated by Real-time polymerase chain reaction (PCR) and Enzyme linked immunosorbent assay (ELISA). Results: Ang II caused sharply increase of the nuclear translocation of NF-κB p65, also, Ang II significantly increased expression of ICAM-1 and MCP-1 secreted by HASMCs, this effect could be inhibited by Ang-(1–7) and IRB. Conclusion: These data may indicate the effect of Ang II on inducing HASMCs to secret ICAM-1 and MCP-1, which might be through the NF-κB p65 pathway by AT-1 receptor

Comparison of Axial Flow and Swirling Flow on Particle Pickup in Horizontal Pneumatic Conveying

Pneumatic conveying is widely used in coal mining. As the lowest conveying velocity of materials, the pickup velocity is the key to the study of gas–solid two-phase flow. In this study, the pickup velocity of pebble particles was experimentally investigated. When the particle size is 3–9 mm, the airflow velocity was found to suitably describe the results as a function of the pickup velocity and have a high correlation. When the swirl number is 0.2, the optimal swirl number was found for which the highest particle pickup ratio was observed. Based on four different methods, namely, visual observation, mass weighing, coefficient of difference analysis, and determination of the peak-average ratio of the pressure drop in the flow field to measure the pickup velocity of the spraying material, the results showed that the accuracy of the particle pickup velocity obtained through visual observation was the lowest, and when the mass–loss rate of the particle was selected as the measurement index of the pickup velocity, the accuracy was the highest. The results will help to realize the long-distance transportation of spraying materials in inclined roadway under the shaft

Clinical Evaluation of a Fluorescence Immunochromatographic Assay for Interleukin-6 Detection

Objective  To evaluate the performance of a new fluorescence immunochromatographic assay for interleukin-6 (IL-6) detection.  Methods  A total of 104 serum samples from patients with suspected infections and normal population were collected at Peking Union Medical College Hospital in December, 2021. Assay A (Wanfu biofluorescence immunochromatography assay) and B (Siemens chemiluminescence assay) were used as references to evaluate the equivalence of the Innova assay for detecting serum IL-6. Meanwhile, 39 homologous paired plasma and serum were collected to evaluate the consistency of the Innova assay in detecting IL-6 content in different types of samples.  Results  In quantitative analysis, two samples were excluded because the content was above the limit of detection for method B. Compared with the assay A or B, the regression equations of the Innova assay were Y=-7.0950+1.1924X (R2=0.9448), Y=-2.6143+1.3072X (R2=0.9391), and the Pearson correlation coefficients were 0.9720 and 0.9691, respectively. With assay A or B as reference, the Innova assay showed the Bland-Altman bias values of -3.0 and 8.0, while the expected bias at the medical decision level (7 ng/L) was -1.44 ng/L (95% CI: -5.37 to 2.50), 1.97 ng/L (95% CI: -2.09 to 6.01), respectively, with 95% CI intervals all included permissible errors (±15%, -1.05 to 1.05 ng/L). The qualitative results showed the high total coincidence rate between the Innova assay and assay A (84.6%), and assay B (83.7%). In addition, the consistency of the Innova assay was good for 39 homologous pairs of plasma and serum samples (P < 0.0001). Only one data (2.6%, 1/39) was identified by Bland-Altman bias analysis to be outside the maximum allowable error range.  Conclusions  Compared with the two reference assays with the same or different principles used in clinical practice, the Innova assay for IL-6 detection has good consistency and correlation, and its test results are almost not affected by the type of serum/plasma samples. To a certain extent, the Innova assay meets the needs of rapid detection in clinical practice

A survey on semantic communications: Technologies, solutions, applications and challenges

Semantic Communication (SC) has emerged as a novel communication paradigm that provides a receiver with meaningful information extracted from the source to maximize information transmission throughput in wireless networks, beyond the theoretical capacity limit. Despite the extensive research on SC, there is a lack of comprehensive survey on technologies, solutions, applications, and challenges for SC. In this article, the development of SC is first reviewed and its characteristics, architecture, and advantages are summarized. Next, key technologies such as semantic extraction, semantic encoding, and semantic segmentation are discussed and their corresponding solutions in terms of efficiency, robustness, adaptability, and reliability are summarized. Applications of SC to UAV communication, remote image sensing and fusion, intelligent transportation, and healthcare are also presented and their strategies are summarized. Finally, some challenges and future research directions are presented to provide guidance for further research of SC