XGV-BERT: Leveraging Contextualized Language Model and Graph Neural Network for Efficient Software Vulnerability Detection

With the advancement of deep learning (DL) in various fields, there are many attempts to reveal software vulnerabilities by data-driven approach. Nonetheless, such existing works lack the effective representation that can retain the non-sequential semantic characteristics and contextual relationship of source code attributes. Hence, in this work, we propose XGV-BERT, a framework that combines the pre-trained CodeBERT model and Graph Neural Network (GCN) to detect software vulnerabilities. By jointly training the CodeBERT and GCN modules within XGV-BERT, the proposed model leverages the advantages of large-scale pre-training, harnessing vast raw data, and transfer learning by learning representations for training data through graph convolution. The research results demonstrate that the XGV-BERT method significantly improves vulnerability detection accuracy compared to two existing methods such as VulDeePecker and SySeVR. For the VulDeePecker dataset, XGV-BERT achieves an impressive F1-score of 97.5%, significantly outperforming VulDeePecker, which achieved an F1-score of 78.3%. Again, with the SySeVR dataset, XGV-BERT achieves an F1-score of 95.5%, surpassing the results of SySeVR with an F1-score of 83.5%

TextANIMAR: Text-based 3D Animal Fine-Grained Retrieval

3D object retrieval is an important yet challenging task, which has drawn more and more attention in recent years. While existing approaches have made strides in addressing this issue, they are often limited to restricted settings such as image and sketch queries, which are often unfriendly interactions for common users. In order to overcome these limitations, this paper presents a novel SHREC challenge track focusing on text-based fine-grained retrieval of 3D animal models. Unlike previous SHREC challenge tracks, the proposed task is considerably more challenging, requiring participants to develop innovative approaches to tackle the problem of text-based retrieval. Despite the increased difficulty, we believe that this task has the potential to drive useful applications in practice and facilitate more intuitive interactions with 3D objects. Five groups participated in our competition, submitting a total of 114 runs. While the results obtained in our competition are satisfactory, we note that the challenges presented by this task are far from being fully solved. As such, we provide insights into potential areas for future research and improvements. We believe that we can help push the boundaries of 3D object retrieval and facilitate more user-friendly interactions via vision-language technologies.Comment: arXiv admin note: text overlap with arXiv:2304.0573

Highly Effective Degradation of Nitrophenols by Biometal Nanoparticles Synthesized using Caulis Spatholobi Extract

The green biosynthesis of metal nanoparticles (MNPs) has been proved to have many advantages over other methods due to its simplicity, large-scale production, ecofriendly approach, and high catalytic efficiency. This work describes a single-step technique for green synthesis of colloidal silver (AgNPs) and gold nanoparticles (AuNPs) using the extract from Caulis Spatholobi stems. Ultraviolet-visible spectroscopy measurements were used to optimize the main synthesis factors, including metal ion concentration, reaction time, and reaction temperature via surface plasmon resonance phenomenon. Fourier-transform infrared spectroscopy showed the possible functional groups responsible for reducing and stabilizing the synthesized MNPs. The powder X-ray diffraction and selected area electron diffraction analysis confirmed the crystalline nature of the biosynthesized MNPs. High-resolution transmission electron microscopy revealed the spherical shape of MNPs with an average size of 10-20 nm. The obtained MNPs also exhibited the enhanced catalytic activity in the reduction of 2-nitrophenol and 3-nitrophenol

Silver and Gold Nanoparticles from Limnophila rugosa Leaves: Biosynthesis, Characterization, and Catalytic Activity in Reduction of Nitrophenols

This study describes a simple green method for the synthesis of Limnophila rugosa leaf-extract-capped silver and gold nanoparticles without using any expensive toxic reductant or stabilizer. The noble metal nanoparticles were characterized by Fourier transform infrared (FTIR) microscopy, powder X-ray diffraction (XRD), field emission scanning electron microscopy (FE-SEM), energy-dispersive X-ray analysis (EDX), high-resolution transmission electron microscopy (HR-TEM), selected area electron diffraction (SAED), and dynamic light scattering (DLS) method. It has been found that the biosynthesized silver and gold nanoparticles are nearly spherical in shape with a mean particle size distribution of 87.5 nm and 122.8 nm, respectively. XRD and SAED patterns confirmed the crystalline nanostructure of the metal nanoparticles. FTIR spectra revealed the functional groups of biomolecules presented in the extract possibly responsible for reducing metallic ions and stabilizing formed nanoparticles. The biosynthesized metal nanoparticles have potential application in catalysis. Compared to previous reports, Limnophila rugosa leaf-extract-capped silver and gold nanoparticles exhibited a good catalytic activity in the reduction of several derivatives of nitrophenols including 1,4-dinitrobenzene, 2-nitrophenol, 3-nitrophenol, and 4-nitrophenol