Watermarking Classification Dataset for Copyright Protection

Substantial research works have shown that deep models, e.g., pre-trained models, on the large corpus can learn universal language representations, which are beneficial for downstream NLP tasks. However, these powerful models are also vulnerable to various privacy attacks, while much sensitive information exists in the training dataset. The attacker can easily steal sensitive information from public models, e.g., individuals' email addresses and phone numbers. In an attempt to address these issues, particularly the unauthorized use of private data, we introduce a novel watermarking technique via a backdoor-based membership inference approach named TextMarker, which can safeguard diverse forms of private information embedded in the training text data. Specifically, TextMarker only requires data owners to mark a small number of samples for data copyright protection under the black-box access assumption to the target model. Through extensive evaluation, we demonstrate the effectiveness of TextMarker on various real-world datasets, e.g., marking only 0.1% of the training dataset is practically sufficient for effective membership inference with negligible effect on model utility. We also discuss potential countermeasures and show that TextMarker is stealthy enough to bypass them

Lightweight and privacy-aware fine-grained access control for IoT-oriented smart health

tru

In Situ TEM Study of the Degradation of PbSe Nanocrystals in Air

PbSe nanocrystals have attracted widespread attention due to a variety of potential applications. However, the practical utility of these nanocrystals has been hindered by their poor air stability, which induces undesired changes in the optical and electronic properties. An understanding of the degradation of PbSe nanocrystals when they are exposed to air is critical for improving the stability and enhancing their applications. Here, we use in situ transmission electron microscopy (TEM) with an environmental cell connected to air to study PbSe nanocrystal degradation triggered by air exposure. We have also conducted a series of complementary studies, including in situ environmental TEM study of PbSe nanocrystals exposed to pure oxygen and PbSe nanocrystals in H2O using a liquid cell, and ex situ experiments, such as O2 plasma treatment and thermal heating of PbSe nanocrystals under different air exposure. Our in situ observations reveal that when PbSe nanocrystals are exposed to air (or oxygen) under electron beam irradiation, they experience a series of changes, including shape evolution of individual nanocrystals with the cuboid intermediates, coalescence between nanocrystals, and formation of PbSe thin films through drastic solid-state fusion. Further studies show that the PbSe thin films transform into an amorphous Pb rich phase or eventually pure Pb, which suggest that Se reacts with oxygen and can be evaporated under electron beam illumination. These various in situ and ex situ experimental results indicate that PbSe nanocrystal degradation in air is initiated by the dissociation and removal of ligands from the PbSe nanocrystal surface

A Weakly Supervised Gas-Path Anomaly Detection Method for Civil Aero-Engines Based on Mapping Relationship Mining of Gas-Path Parameters and Improved Density Peak Clustering

Gas-path anomalies account for more than 90% of all civil aero-engine anomalies. It is essential to develop accurate gas-path anomaly detection methods. Therefore, a weakly supervised gas-path anomaly detection method for civil aero-engines based on mapping relationship mining of gas-path parameters and improved density peak clustering is proposed. First, the encoder-decoder, composed of an attention mechanism and a long short-term memory neural network, is used to construct the mapping relationship mining model among gas-path parameters. The predicted values of gas-path parameters under the restriction of mapping relationships are obtained. The deviation degree from the original values to the predicted values is regarded as the feature. To force the extracted features to better reflect the anomalies and make full use of weakly supervised labels, a weakly supervised cross-entropy loss function under extreme class imbalance is deployed. This loss function can be combined with a simple classifier to significantly improve the feature extraction results, in which anomaly samples are more different from normal samples and do not reduce the mining precision. Finally, an anomaly detection method is deployed based on improved density peak clustering and a weakly supervised clustering parameter adjustment strategy. In the improved density peak clustering method, the local density is enhanced by K-nearest neighbors, and the clustering effect is improved by a new outlier threshold determination method and a new outlier treatment method. Through these settings, the accuracy of dividing outliers and clustering can be improved, and the influence of outliers on the clustering process reduced. By introducing weakly supervised label information and automatically iterating according to clustering and anomaly detection results to update the hyperparameter settings, a weakly supervised anomaly detection method without complex parameter adjustment processes can be implemented. The experimental results demonstrate the superiority of the proposed method

Transcriptomic analysis of gills provides insights into the molecular basis of molting in Chinese mitten crab (Eriocheir sinensis)

Chinese mitten crab (Eriocheir sinensis) is an economically important freshwater aquaculture species and is a model species for research on the mechanism of molting. This study aimed to identify important candidate genes associated with the molting process and to determine the role of gills in the regulation of molting with the help of transcriptomic analysis. The transcriptomes of crabs at different molting stages—postmolt (PoM), intermolt (InM), premolt (PrM) and ecdysis (E)—were de novo assembled to generate 246,232 unigenes with a mean length of 851 bp. A total of 86,634 unigenes (35.18% of the total unigenes) were annotated against reference databases. Significantly upregulated genes were identified in postmolt compared to intermolt (1,475), intermolt compared to premolt (65), premolt compared to ecdysis (1,352), and ecdysis compared to postmolt (153), and the corresponding numbers of downregulated genes were 1,276, 32, 1,573 and 171, respectively. Chitin synthase, endochitinase, chitinase A, chitinase 3, chitinase 6 and chitin deacetylase 1 were upregulated during the postmolt and ecdysis stages, while phosphoglucomutase 3 (PGM3), glucosamine 6-phosphate deaminase (GNPDA) and glucosamine glycoside hydrolase (nagZ) were upregulated during the intermolt and premolt stages compared to the other stages. The upregulated genes were enriched in several lipid-related metabolic pathways, such as “fatty acid elongation”, “glycerophospholipid metabolism” and “sulfur metabolism”. Meanwhile, three signaling pathways, including the “phosphatidylinositol signaling system”, the “calcium signaling pathway” and the “GnRH signaling pathway” were also enriched. Tetraspanin-18, an important effector gene in the lysosomal pathway involved in cell apoptosis, up-regulate with the beginning of molting (in premolt stage) and reach the top in the ecdysis stage, and barely expressed in the intermolt stage. The expression variations in the tetraspanin-18 gene indicated that it may play an important role in the beginning of molting cycle, which might be regulated by the stress of salinity. This study revealed that the gills could participate in chitin degradation, in reestablishment of the exoskeleton and the signaling process. Based on transcriptomic analysis of the gills, we not only explored novel molecular mechanisms of molting in E. sinensis but also acquired foundational genetic data for E. sinensis

Inserting ultrafine NiO nanoparticles into amorphous NiP sheets by in-situ phase reconstruction for high-stability of the HER catalysts

Abstract: The P-based electrode electrocatalysts have exhibited high activities for the hydrogen evolution reaction (HER), but their structural stabilities in the long-term operation of water electrolysis pose a technical challenge for industrial-scale applications. In this study, amorphous NiP sheet arrays with rich active sites were created on nickel foam (NF) by in situ phase reconstruction, and then NiO ultrafine particles were generated within the NiP sheets. The array electrode exhibited not only enhanced catalytic activity verified by 76 mV of HER for NiO@NiP/NF at 10 mA cm−2, but also excellent structural stability in 1 M KOH solution proved by the fact that the structure of the assembled electrode remained intact after long-term operation at 100 mA cm−2 for 120 h

Correction: Layer-structured FeCo bihydroxide as an ultra-stable bifunctional electrocatalyst for water splitting at high current densities

The development of stable bifunctional electrodes capable of operation at high current densities is a key requirement for large scale hydrogen generation by water electrolysis. Herein, amorphous FeCo hydroxides are controllably electroplated onto nickel mesh to produce binder-free bifunctional FeCo-LDH/NM electrodes for water splitting. In an alkaline electrolyte, the hydrogen evolution reaction on FeCo-LDH/NM requires an overpotential of only 311 mV to deliver a current density of 1000 mA cm−2, and the same current density is achieved in the oxygen evolution reaction at 300 mV. Notably, in a real electrolyzer setup, a current density of 1000 mA cm−2 is realized at 1.82 V and remains unchanged for 150 h. The study demonstrates promising bifunctional electrocatalytic properties of the FeCo-LDH/NM electrode material making it a suitable candidate for practical applications in large-scale water electrolysis systems

Membership Inference via Backdooring

Recently issued data privacy regulations like GDPR (General Data Protection Regulation) grant individuals the right to be forgotten. In the context of machine learning, this requires a model to forget about a training data sample if requested by the data owner (i.e., machine unlearning). As an essential step prior to machine unlearning, it is still a challenge for a data owner to tell whether or not her data have been used by an unauthorized party to train a machine learning model. Membership inference is a recently emerging technique to identify whether a data sample was used to train a target model, and seems to be a promising solution to this challenge. However, straightforward adoption of existing membership inference approaches fails to address the challenge effectively due to being originally designed for attacking membership privacy and suffering from several severe limitations such as low inference accuracy on well-generalized models. In this paper, we propose a novel membership inference approach inspired by the backdoor technology to address the said challenge. Specifically, our approach of Membership Inference via Backdooring (MIB) leverages the key observation that a backdoored model behaves very differently from a clean model when predicting on deliberately marked samples created by a data owner. Appealingly, MIB requires data owners' marking a small number of samples for membership inference and only black-box access to the target model, with theoretical guarantees for inference results. We perform extensive experiments on various datasets and deep neural network architectures, and the results validate the efficacy of our approach, e.g., marking only 0.1% of the training dataset is practically sufficient for effective membership inference.Comment: This paper has been accepted by IJCAI-2

Remembered or Forgotten?-An EEG-Based Computational Prediction Approach.

Prediction of memory performance (remembered or forgotten) has various potential applications not only for knowledge learning but also for disease diagnosis. Recently, subsequent memory effects (SMEs)-the statistical differences in electroencephalography (EEG) signals before or during learning between subsequently remembered and forgotten events-have been found. This finding indicates that EEG signals convey the information relevant to memory performance. In this paper, based on SMEs we propose a computational approach to predict memory performance of an event from EEG signals. We devise a convolutional neural network for EEG, called ConvEEGNN, to predict subsequently remembered and forgotten events from EEG recorded during memory process. With the ConvEEGNN, prediction of memory performance can be achieved by integrating two main stages: feature extraction and classification. To verify the proposed approach, we employ an auditory memory task to collect EEG signals from scalp electrodes. For ConvEEGNN, the average prediction accuracy was 72.07% by using EEG data from pre-stimulus and during-stimulus periods, outperforming other approaches. It was observed that signals from pre-stimulus period and those from during-stimulus period had comparable contributions to memory performance. Furthermore, the connection weights of ConvEEGNN network can reveal prominent channels, which are consistent with the distribution of SME studied previously