Robust deep semi-supervised learning with label propagation and differential privacy

Semi-supervised learning (SSL) methods provide a powerful tool for utilizing abundant unlabeled data to strengthen standard supervised learning. Traditional graph-based SSL methods prevail in classical SSL problems for their intuitional implementation and effective performance. However, they encounter troubles when applying to image classification followed by modern deep learning, since the diffusion algorithms face the curse of dimensionality. In this study, we propose a simple and efficient SSL method, combining a graph-based SSL paradigm with differential privacy. We aim at developing coherent latent feature space of deep neural networks so that the diffusion algorithm in the latent space can give more precise predictions for unlabeled data. Our approach achieves state-of-the-art performance on the Cifar10, Cifar100, and Mini-imagenet benchmark datasets and obtains an error rate of 18.56% on Cifar10 using only 1% of all labels. Furthermore, our approach inherits the benefits of graph-based SSL methods with a simple training process and can be easily combined with any network architecture

DiQAD: A Benchmark Dataset for End-to-End Open-domain Dialogue Assessment

Dialogue assessment plays a critical role in the development of open-domain dialogue systems. Existing work are uncapable of providing an end-to-end and human-epistemic assessment dataset, while they only provide sub-metrics like coherence or the dialogues are conversed between annotators far from real user settings. In this paper, we release a large-scale dialogue quality assessment dataset (DiQAD), for automatically assessing open-domain dialogue quality. Specifically, we (1) establish the assessment criteria based on the dimensions conforming to human judgements on dialogue qualities, and (2) annotate large-scale dialogues that conversed between real users based on these annotation criteria, which contains around 100,000 dialogues. We conduct several experiments and report the performances of the baselines as the benchmark on DiQAD. The dataset is openly accessible at https://github.com/yukunZhao/Dataset_Dialogue_quality_evaluation.Comment: Accepted to Findings of EMNLP 202

AOC-IDS: Autonomous Online Framework with Contrastive Learning for Intrusion Detection

The rapid expansion of the Internet of Things (IoT) has raised increasing concern about targeted cyber attacks. Previous research primarily focused on static Intrusion Detection Systems (IDSs), which employ offline training to safeguard IoT systems. However, such static IDSs struggle with real-world scenarios where IoT system behaviors and attack strategies can undergo rapid evolution, necessitating dynamic and adaptable IDSs. In response to this challenge, we propose AOC-IDS, a novel online IDS that features an autonomous anomaly detection module (ADM) and a labor-free online framework for continual adaptation. In order to enhance data comprehension, the ADM employs an Autoencoder (AE) with a tailored Cluster Repelling Contrastive (CRC) loss function to generate distinctive representation from limited or incrementally incoming data in the online setting. Moreover, to reduce the burden of manual labeling, our online framework leverages pseudo-labels automatically generated from the decision-making process in the ADM to facilitate periodic updates of the ADM. The elimination of human intervention for labeling and decision-making boosts the system's compatibility and adaptability in the online setting to remain synchronized with dynamic environments. Experimental validation using the NSL-KDD and UNSW-NB15 datasets demonstrates the superior performance and adaptability of AOC-IDS, surpassing the state-of-the-art solutions. The code is released at https://github.com/xinchen930/AOC-IDS

Knowing What LLMs DO NOT Know: A Simple Yet Effective Self-Detection Method

Large Language Models (LLMs) have shown great potential in Natural Language Processing (NLP) tasks. However, recent literature reveals that LLMs generate nonfactual responses intermittently, which impedes the LLMs' reliability for further utilization. In this paper, we propose a novel self-detection method to detect which questions that a LLM does not know that are prone to generate nonfactual results. Specifically, we first diversify the textual expressions for a given question and collect the corresponding answers. Then we examine the divergencies between the generated answers to identify the questions that the model may generate falsehoods. All of the above steps can be accomplished by prompting the LLMs themselves without referring to any other external resources. We conduct comprehensive experiments and demonstrate the effectiveness of our method on recently released LLMs, e.g., Vicuna, ChatGPT, and GPT-4

Improved sliding mode direct power control for low-carbon oriented MMC-HVDC of asymmetric offshore wind power flexible systems

The modular multilevel converter based high voltage direct current (MMC-HVDC) is a dynamic power balancing system. The control system of MMC generally adopts a dual closed-loop vector control strategy based on the traditional instantaneous power model under asymmetric grid state, which has complex control structure and low control accuracy. This paper introduces a flexible instantaneous power model and establishes a general power equation with active power and new reactive power as control objects. Based on this, an improved sliding-mode MMC-HVDC direct power control strategy based on the new instantaneous power model is proposed which combines the flexible instantaneous power model and the improved sliding-mode control method to eliminate the twice grid-frequency ripples in both active and reactive power under asymmetric grid states. Furthermore, it omits the inner-loop controller and power compensation terms while optimizing the control structure. Simulation results show that the proposed method has better dynamic responsiveness, control accuracy and robustness under operating conditions such as asymmetric grid state and parameter perturbation which can better exploit the advantages of the flexible instantaneous power model

Go@Se@ni cathode materials for lithium-selenium battery

Selenium is a promising cathode material for high-energy lithium batteries. In this work, selenium was electrodeposited on nickel foam from aqueous selenite solution. The influences of pH values and current density on electrodeposited Se@Ni were investigated. It is found that electrodeposition at pH 7 and 0.5 mA cm −2 enables high current efficiency and produces uniform and smooth deposits. Graphene oxide (GO) was further coated on Se@Ni through physical adsorption to produce GO@Se@Ni. The developed GO@Se@Ni electrode delivers a high initial specific capacity of 593 mAh g −1 and good capacity retention over 100 cycles at 0.1 C

PUMA: Secure Inference of LLaMA-7B in Five Minutes

With ChatGPT as a representative, tons of companies have began to provide services based on large Transformers models. However, using such a service inevitably leak users' prompts to the model provider. Previous studies have studied secure inference for Transformer models using secure multiparty computation (MPC), where model parameters and clients' prompts are kept secret. Despite this, these frameworks are still limited in terms of model performance, efficiency, and deployment. To address these limitations, we propose framework PUMA to enable fast and secure Transformer model inference. Our framework designs high quality approximations for expensive functions, such as GeLU and Softmax, which significantly reduce the cost of secure inference while preserving the model performance. Additionally, we design secure Embedding and LayerNorm procedures that faithfully implement the desired functionality without undermining the Transformer architecture. PUMA is about 2x faster than the state-of-the-art MPC framework MPCFORMER(ICLR 2023) and has similar accuracy as plaintext models without fine-tuning (which the previous works failed to achieve). One more thing, PUMA can evaluate LLaMA-7B in around 5 minutes to generate 1 token. To our best knowledge, this is the first time that a model with such a parameter size is able to be evaluated under MPC. PUMA has been open-sourced in the Github repository of SecretFlow-SPU

Gut microbiota mediates positive effects of liraglutide on dyslipidemia in mice fed a high-fat diet

Except for improving glycemic control, liraglutide, one of the glucagon-like peptide-1 receptor agonists, has exerted promising therapeutic effects for dyslipidemia. It has been proved that gut microbiota plays a dramatic role in regulating lipid metabolism. This study aims to explore whether liraglutide could improve dyslipidemia by modulating the gut microbiota in mice fed a high-fat diet (HFD). The C57BL/6 mice were fed a HFD to establish an animal model of dyslipidemia, and then administered with liraglutide or normal saline (NS) for 12 weeks. Indices of glucolipid metabolism were evaluated. Gut microbiota of the mice was analyzed by 16S rRNA gene sequencing. Compared with HFD group, liraglutide significantly alleviated weight, total cholesterol (TC) and low-density lipoprotein cholesterol (LDL) levels, meanwhile elevating high-density lipoprotein cholesterol (HDL) levels (all p < 0.05). The gut microbiota analysis revealed that liraglutide greatly reduced the relative abundance of Firmicutes and augmented that of Bacteroidetes, with a concomitant drop in the Firmicutes/Bacteroidetes ratio. Meanwhile, liraglutide dramatically changed the overall composition, promoted the growth of beneficial microbes (Akkermansia, Lactobacillus, Parabacteroides, Oscillospira, etc.), and inhibited the growth of harmful microbes (AF12, Shigella, Proteobacteria, Xenorhabdus, etc.). Especially, the relative abundance of Akkermansia increased the most after liraglutide treatment. Correlation analysis suggested that TC and LDL were positively correlated with some harmful bacteria, and negatively associated with beneficial bacteria. This study confirmed that liraglutide had a certain therapeutic effect on dyslipidemia in HFD-fed mice and could regulate the composition of the gut microbiota associated with lipid metabolism, especially Akkermansia. Thus, affecting gut microbiota might be a potential mechanism of liraglutide in attenuating dyslipidemia