A Universal Identity Backdoor Attack against Speaker Verification based on Siamese Network

Speaker verification has been widely used in many authentication scenarios. However, training models for speaker verification requires large amounts of data and computing power, so users often use untrustworthy third-party data or deploy third-party models directly, which may create security risks. In this paper, we propose a backdoor attack for the above scenario. Specifically, for the Siamese network in the speaker verification system, we try to implant a universal identity in the model that can simulate any enrolled speaker and pass the verification. So the attacker does not need to know the victim, which makes the attack more flexible and stealthy. In addition, we design and compare three ways of selecting attacker utterances and two ways of poisoned training for the GE2E loss function in different scenarios. The results on the TIMIT and Voxceleb1 datasets show that our approach can achieve a high attack success rate while guaranteeing the normal verification accuracy. Our work reveals the vulnerability of the speaker verification system and provides a new perspective to further improve the robustness of the system.Comment: Accepted by the Interspeech 2022. The first two authors contributed equally to this wor

Multi-scale Adaptive Fusion Network for Hyperspectral Image Denoising

Removing the noise and improving the visual quality of hyperspectral images (HSIs) is challenging in academia and industry. Great efforts have been made to leverage local, global or spectral context information for HSI denoising. However, existing methods still have limitations in feature interaction exploitation among multiple scales and rich spectral structure preservation. In view of this, we propose a novel solution to investigate the HSI denoising using a Multi-scale Adaptive Fusion Network (MAFNet), which can learn the complex nonlinear mapping between clean and noisy HSI. Two key components contribute to improving the hyperspectral image denoising: A progressively multiscale information aggregation network and a co-attention fusion module. Specifically, we first generate a set of multiscale images and feed them into a coarse-fusion network to exploit the contextual texture correlation. Thereafter, a fine fusion network is followed to exchange the information across the parallel multiscale subnetworks. Furthermore, we design a co-attention fusion module to adaptively emphasize informative features from different scales, and thereby enhance the discriminative learning capability for denoising. Extensive experiments on synthetic and real HSI datasets demonstrate that the proposed MAFNet has achieved better denoising performance than other state-of-the-art techniques. Our codes are available at \verb'https://github.com/summitgao/MAFNet'.Comment: IEEE JSTASRS 2023, code at: https://github.com/summitgao/MAFNe

FedPrompt: Communication-Efficient and Privacy Preserving Prompt Tuning in Federated Learning

Federated learning (FL) has enabled global model training on decentralized data in a privacy-preserving way by aggregating model updates. However, for many natural language processing (NLP) tasks that utilize pre-trained language models (PLMs) with large numbers of parameters, there are considerable communication costs associated with FL. Recently, prompt tuning, which tunes some soft prompts without modifying PLMs, has achieved excellent performance as a new learning paradigm. Therefore we want to combine the two methods and explore the effect of prompt tuning under FL. In this paper, we propose "FedPrompt" as the first work study prompt tuning in a model split learning way using FL, and prove that split learning greatly reduces the communication cost, only 0.01% of the PLMs' parameters, with little decrease on accuracy both on IID and Non-IID data distribution. This improves the efficiency of FL method while also protecting the data privacy in prompt tuning.In addition, like PLMs, prompts are uploaded and downloaded between public platforms and personal users, so we try to figure out whether there is still a backdoor threat using only soft prompt in FL scenarios. We further conduct backdoor attacks by data poisoning on FedPrompt. Our experiments show that normal backdoor attack can not achieve a high attack success rate, proving the robustness of FedPrompt.We hope this work can promote the application of prompt in FL and raise the awareness of the possible security threats

Short-term interval prediction of PV power based on quantile regression-stacking model and tree-structured parzen estimator optimization algorithm

In recent years, the photovoltaic (PV) industry has grown rapidly and the scale of grid-connected PV continues to increase. The random and fluctuating nature of PV power output is beginning to threaten the safe and stable operation of the power system. PV power interval forecasting can provide more comprehensive information to power system decision makers and help to achieve risk control and risk decision. PV power interval forecasting is of great importance to power systems. Therefore, in this study, a Quantile Regression-Stacking (QR-Stacking) model is proposed to implement PV power interval prediction. This integrated model uses three models, extreme gradient boosting (Xgboost), light gradient boosting machine (LightGBM) and categorical boosting (CatBoost), as the base learners and Quantile Regression-Long and Short Term Memory (QR-LSTM) model as the meta-learner. It is worth noting that in order to determine the hyperparameters of the three base learners and one meta-learner, the optimal hyperparameters of the model are searched using a Tree-structured Parzen Estimator (TPE) optimization algorithm based on Bayesian ideas. Meanwhile, the correlation coefficient is applied to determine the input characteristics of the model. Finally, the validity of the proposed model is verified using the actual data of a PV plant in China

MicroRNA-196a-5p targeting LRP1B modulates phenotype of thyroid carcinoma cells

Introduction: Thyroid cancer (TC) is a common endocrine malignancy, comprising nearly one-third of all head and neck malignancies worldwide. MicroRNAs (miRNAs) have been implicated in the malignant progression of multiple cancers; however, their contribution to thyroid diseases has not been fully explored. Material and methods: This study aimed to illustrate the regulatory mechanism of microRNA-196a-5p in TC progression and to investigate whether microRNA-196a-5p affects progression of TC cells by targeting low-density lipoprotein receptor-associated protein 1B (LRP1B). MicroRNA-196a-5p and LRP1B expression status in TC cells and normal human thyroid cells was detected by quantative reverse transcription polymerase chain reaction (qRT-PCR) and western blot. Dual-luciferase reporter assay, cell counting kit-8 (CCK-8) assay, scratch healing assay, and Transwell assay were also performed. Results: The results showed that microRNA-196a-5p expression was up-regulated and LRP1B expression was down regulated in TC cells. In addition, the upregulation of microRNA-196a-5p facilitated progression of TC cells. Silencing microRNA-196a-5p led to the opposite results. Dual-luciferase reporter assay offered evidence for microRNA-196a-5p targeting LRP1B in TC. MicroRNA-196a-5p could target LRP1B to facilitate proliferation, invasion, and migration of TC cells. Conclusion: Overall, this study revealed that microRNA-196a-5p may be a cancer-promoting microRNA that plays an important role in TC progression

Effects of dietary L-Citrulline supplementation on growth performance, meat quality, and fecal microbial composition in finishing pigs

Gut microbiota play an important role in the gut ecology and development of pigs, which is always regulated by nutrients. This study investigated the effect of L-Citrulline on growth performance, carcass characteristics, and its potential regulatory mechanism. The results showed that 1% dietary L-Citrulline supplementation for 52 days significantly increased final weight, liveweight gain, carcass weight, and average backfat and markedly decreased drip loss (p < 0.05) of finishing pigs compared with the control group. Microbial analysis of fecal samples revealed a marked increase in α-diversity and significantly altered composition of gut microbiota in finishing pigs in response to L-Citrulline. In particular, these altered gut microbiota at the phylum and genus level may be mainly involved in the metabolic process of carbohydrate, energy, and amino acid, and exhibited a significant association with final weight, carcass weight, and backfat thickness. Taken together, our data revealed the potential role of L-Citrulline in the modulation of growth performance, carcass characteristics, and the meat quality of finishing pigs, which is most likely associated with gut microbiota

Simulating Composite Delamination with a Damage-Type Cohesive Zone Model

Interlaminar damage (delamination) is one of the predominant forms of failure in laminated composites, which is broadly used in aerospace, astronautical and automobile industry and many other fields. Engineering problems about damage tolerance and structure durability requires the ability to simulate mixed mode delamination in laminated composites. The objective of the research is to develop an implicit scheme for a recently developed damage-type cohesive zone model (CZM) with an associated systematic calibration method. The CZM is formulated based on thermodynamics, and the damage evolution is derived with the principle of maximum dissipation. A stable implicit scheme using the Newton–Raphson method is developed to solve the model iteratively. A finite element framework consisting of double-cantilever beam (DCB) end-notched flexure (ENF) and mixed-mode beam (MMB) models and properly chosen mesh density is built to incorporate the present CZM. A systematic calibration method is then established to calibrate the damage parameters from experimental results of interfacial parameters and flexural tests. The present model is found to yield consistent and accurate results in finite element simulations. Specifically, it\u27s shown to be able to reproduce the critical energy release rates and maximum loads that the structure can endure. The maximum loads are found to be also affected by the interfacial strenghth. Conclusively, the present model could be used in engineering practice because of its superior accuracy and stability