TranssionADD: A multi-frame reinforcement based sequence tagging model for audio deepfake detection

Thanks to recent advancements in end-to-end speech modeling technology, it has become increasingly feasible to imitate and clone a user`s voice. This leads to a significant challenge in differentiating between authentic and fabricated audio segments. To address the issue of user voice abuse and misuse, the second Audio Deepfake Detection Challenge (ADD 2023) aims to detect and analyze deepfake speech utterances. Specifically, Track 2, named the Manipulation Region Location (RL), aims to pinpoint the location of manipulated regions in audio, which can be present in both real and generated audio segments. We propose our novel TranssionADD system as a solution to the challenging problem of model robustness and audio segment outliers in the trace competition. Our system provides three unique contributions: 1) we adapt sequence tagging task for audio deepfake detection; 2) we improve model generalization by various data augmentation techniques; 3) we incorporate multi-frame detection (MFD) module to overcome limited representation provided by a single frame and use isolated-frame penalty (IFP) loss to handle outliers in segments. Our best submission achieved 2nd place in Track 2, demonstrating the effectiveness and robustness of our proposed system

Internet and Telecommunication Fraud Prevention Analysis based on Deep Learning

In recent years, contactless fraud crimes via telecommunication and Internet have grown rapidly. Meanwhile, the rate of solved criminal cases is much lower, which is mainly due to two reasons. Firstly, the definition of risk factors in the field of new Internet and telecommunication fraud crime is not comprehensive, resulting in the problem not being well defined. Secondly, Internet fraud crime information is mostly recorded using natural language with huge volume, and there is a lack of automated and intelligent way to deeply analyze and extract the risk factor. To better analyze the Internet and telecommunication fraud crime to help solve more cases, in this paper, we propose a new Internet and telecommunication fraud crime risk factor extraction system. After studying the existing related research, we propose a novel risk factor extraction technology based on BERT. This novel technology can gracefully deal with multi-sources and heterogeneous data problems during the extraction of risk factors in multiple dimensions; meanwhile, it can significantly reduce the need for computation resources and improve the online serving performance. After experimentation, this technique can significantly reduce training time by 60%-70%, and meanwhile, it can reduce the computation resources by 80% and improve serving performance by 5 times during serving. In our approach, we propose a novel approach to set sample weight and loss weight based on data characteristics and data distribution during model training, which can significantly improve extraction precision. With adjusting the sample weight during model training, we can get 1.56% precision improved. Moreover, setting the loss weight during model training, the precision can be improved by 1.63% compared to baseline mode

Genetic Architecture of Natural Variation in Rice Nonphotochemical Quenching Capacity Revealed by Genome-Wide Association Study

The photoprotective processes conferred by nonphotochemical quenching (NPQ) serve fundamental roles in maintaining plant fitness and sustainable yield. So far, few loci have been reported to be involved in natural variation of NPQ capacity in rice (Oryza sativa), and the extents of variation explored are very limited. Here we conducted a genome-wide association study (GWAS) for NPQ capacity using a diverse worldwide collection of 529 O. sativa accessions. A total of 33 significant association loci were identified. To check the validity of the GWAS signals, three F2 mapping populations with parents selected from the association panel were constructed and assayed. All QTLs detected in mapping populations could correspond to at least one GWAS signal, indicating the GWAS results were quite reliable. OsPsbS1 was repeatedly detected and explained more than 40% of the variation in the whole association population in two years, and demonstrated to be a common major QTL in all three mapping populations derived from inter-group crosses. We revealed 43 single nucleotide polymorphisms (SNPs) and 7 insertions and deletions (InDels) within a 6,997-bp DNA fragment of OsPsbS1, but found no non-synonymous SNPs or InDels in the coding region, indicating the PsbS1 protein sequence is highly conserved. Haplotypes with the 2,674-bp insertion in the promoter region exhibited significantly higher NPQ values and higher expression levels of OsPsbS1. The OsPsbS1 RNAi plants and CRISPR/Cas9 mutants exhibited drastically decreased NPQ values. OsPsbS1 had specific and high-level expression in green tissues of rice. However, we didn't find significant function for OsPsbS2, the other rice PsbS homologue. Manipulation of the significant loci or candidate genes identified may enhance photoprotection and improve photosynthesis and yield in rice

Expression Regulation of Starch and Storage Protein Synthesis Related Genes in Rice Grains

Starch and the storage proteins are the main nutritious substances in crop grains, and their composition and content in grains play a decisive role in the grain quality of rice and other staple food crops. This review has mainly summarized the new advances in the expression regulation of starch and storage protein synthesis related genes in rice grains. Moreover, the challenges of the starch and storage protein synthesis substances in rice genetic improvement were also discussed. This review will provide important information for genetic improvement of grain quality in rice and, potentially, other staple cereals