DialogXL: All-in-One XLNet for Multi-Party Conversation Emotion Recognition

This paper presents our pioneering effort for emotion recognition in conversation (ERC) with pre-trained language models. Unlike regular documents, conversational utterances appear alternately from different parties and are usually organized as hierarchical structures in previous work. Such structures are not conducive to the application of pre-trained language models such as XLNet. To address this issue, we propose an all-in-one XLNet model, namely DialogXL, with enhanced memory to store longer historical context and dialog-aware self-attention to deal with the multi-party structures. Specifically, we first modify the recurrence mechanism of XLNet from segment-level to utterance-level in order to better model the conversational data. Second, we introduce dialog-aware self-attention in replacement of the vanilla self-attention in XLNet to capture useful intra- and inter-speaker dependencies. Extensive experiments are conducted on four ERC benchmarks with mainstream models presented for comparison. The experimental results show that the proposed model outperforms the baselines on all the datasets. Several other experiments such as ablation study and error analysis are also conducted and the results confirm the role of the critical modules of DialogXL.Comment: Accepted by AAAI 2021 main conferenc

White-Box Adversarial Attacks on Deep Learning-Based Radio Frequency Fingerprint Identification

Radio frequency fingerprint identification (RFFI) is an emerging technique for the lightweight authentication of wireless Internet of things (IoT) devices. RFFI exploits unique hardware impairments as device identifiers, and deep learning is widely deployed as the feature extractor and classifier for RFFI. However, deep learning is vulnerable to adversarial attacks, where adversarial examples are generated by adding perturbation to clean data for causing the classifier to make wrong predictions. Deep learning-based RFFI has been shown to be vulnerable to such attacks, however, there is currently no exploration of effective adversarial attacks against a diversity of RFFI classifiers. In this paper, we report on investigations into white-box attacks (non-targeted and targeted) using two approaches, namely the fast gradient sign method (FGSM) and projected gradient descent (PGD). A LoRa testbed was built and real datasets were collected. These adversarial examples have been experimentally demonstrated to be effective against convolutional neural networks (CNNs), long short-term memory (LSTM) networks, and gated recurrent units (GRU).Comment: 6 pages, 9 figures, Accepeted by International Conference on Communications 202

Radio Frequency Fingerprint Identification for Security in Low-Cost IoT Devices

Radio frequency fingerprint identification (RFFI) can uniquely classify wireless devices by analyzing the received signal distortions caused by the intrinsic hardware impairments. The state-of-the-art deep learning techniques such as convolutional neural network (CNN) have been adopted to classify IoT devices with high accuracy. However, deep learning-based RFFI requires input data of a fixed size. In addition, many IoT devices work in low signal-to-noise ratio (SNR) scenarios but the low SNR RFFI is rarely investigated. In this paper, the state-of-the-art transformer model is used as the classifier, which can process signals of variable length. Data augmentation is adopted to improve low SNR RFFI performance. A multi-packet inference approach is further proposed to improve the classification accuracy in low SNR scenarios. We take LoRa as a case study and evaluate the system by classifying 10 commercial-off-the-shelf LoRa devices in various SNR conditions. The online augmentation can boost the low SNR RFFI performance by up to 50% and multi-packet inference can further increase it by over 20%.acceptedVersionPeer reviewe

Radio Frequency Fingerprint Identification for Security in Low-Cost IoT Devices

Radio frequency fingerprint identification (RFFI) can uniquely classify wireless devices by analyzing the received signal distortions caused by the intrinsic hardware impairments. The state-of-the-art deep learning techniques such as convolutional neural network (CNN) have been adopted to classify IoT devices with high accuracy. However, deep learning-based RFFI requires input data of a fixed size. In addition, many IoT devices work in low signal-to-noise ratio (SNR) scenarios but the low SNR RFFI is rarely investigated. In this paper, the state-of-the-art transformer model is used as the classifier, which can process signals of variable length. Data augmentation is adopted to improve low SNR RFFI performance. A multi-packet inference approach is further proposed to improve the classification accuracy in low SNR scenarios. We take LoRa as a case study and evaluate the system by classifying 10 commercial-off-the-shelf LoRa devices in various SNR conditions. The online augmentation can boost the low SNR RFFI performance by up to 50% and multi-packet inference can further increase it by over 20%

Radio Frequency Fingerprint Identification for LoRa Using Spectrogram and CNN

Radio frequency fingerprint identification (RFFI) is an emerging device authentication technique that relies on intrinsic hardware characteristics of wireless devices. We designed an RFFI scheme for Long Range (LoRa) systems based on spectrogram and convolutional neural network (CNN). Specifically, we used spectrogram to represent the fine-grained time-frequency characteristics of LoRa signals. In addition, we revealed that the instantaneous carrier frequency offset (CFO) is drifting, which will result in misclassification and significantly compromise the system stability; we demonstrated CFO compensation is an effective mitigation. Finally, we designed a hybrid classifier that can adjust CNN outputs with the estimated CFO. The mean value of CFO remains relatively stable, hence it can be used to rule out CNN predictions whose estimated CFO falls out of the range. We performed experiments in real wireless environments using 20 LoRa devices under test (DUTs) and a Universal Software Radio Peripheral (USRP) N210 receiver. By comparing with the IQ-based and FFT-based RFFI schemes, our spectrogram-based scheme can reach the best classification accuracy, i.e., 97.61% for 20 LoRa DUTs.Comment: Accepted for publication in IEEE INFOCOM 202

Multi-Channel CNN-Based Open-Set RF Fingerprint Identification for LTE Devices

Radio frequency fingerprint identification (RFFI) is a promising technique that exploits the transmitter-specific characteristics of the RF chain for identification. Disregarding its massive deployment, long-term evolution (LTE) systems have not fully benefited from RFFI. In this paper, an RFFI technique is designed to authenticate LTE devices. Three segments of the LTE physical layer random access channel (PRACH) preambles are captured, namely the transient-on, transient-off, and modulation parts. The segments are first converted into differential constellation trace figures (DCTFs), and then a specific type of neural network called multi-channel convolutional neural network (MCCNN) is used for identification. Additionally, the protocol is able to be applied for open-set identification, i.e., unknown device detection. Experiments are conducted with ten LTE mobile phones. The results show that the proposed RFFI scheme is robust against location changes. In the known device classification problem, the classification accuracy can reach 98.70% in the line-of-sight (LOS) scenario and 89.40% in the non-line-of-sight (NLOS) scenario. In the open-set unknown device detection problem, the identification equal error rate (EER) and area under the curve (AUC) reach 0.0545 and 0.9817, respectively, among six known devices and four unknown devices

Orthogonal optimization of the ratio of nano-silica sol-EVA-fly ash cement-based composite slurry and the effect on its physical properties

To address the problem that traditional cement-based slurry materials cannot meet the actual demand for grouting and reinforcement of large deformation roadways in coal mines, some high-performance composite slurry materials are obtained by modifying ordinary Portland cement with nano-silica sol, ethylene-vinyl acetate copolymer (EVA) and fly ash. The orthogonal test and extreme difference analysis are used to systematically study the variation of the physical and mechanical properties of the composite slurries, determine the optimal ratio, and further analyze the difference in the physical properties between the optimal ratio of composite slurries and pure cement, construct the hydration reaction mechanism model of the composite slurries, and elucidate the mechanical properties of the composite slurries for reinforcing broken rocks. The results of the study show that the optimum proportion of composite slurry is 0.7 water-cement ratio, 15% fly ash, 2% silica sol and 7.5% EVA. Compared with pure cement, the rheology of composite slurry is slightly decreased, but the stability and mechanical properties of the slurry are significantly improved, with the initial setting time shortened by 38.9%, the final setting time shortened by 53.8%, the precipitation rate reduced by 60%, the stone rate increased by 3.3%, the uniaxial compressive strength increased by 39.1%, the tensile strength increased by 97.2%, and the tensile/compression ratio increased by 41.7%. Silica sol and fly ash undergo volcanic ash reaction with Ca(OH)2 to generate more calcium silicate hydrate (C-S-H) and calcium aluminate hydrate (C-A-H) at different times, which promotes the hydration reaction of the composite slurry and accelerates the film formation of EVA to make the stone body more dense. The injection volume of the composite grout and the uniaxial compressive strength of the bonded body both increase with the increasing grouting pressure. With the increase in the Talbot index, the compressive strength first increases and then decreases. The failure mode is often characterized by bulging, and shear dilation deformation is pronounced. When the grouting pressure exceeds 2 MPa and the Talbot index is 0.5, the bond strength of the grout is higher, and the damage is reduced. This study provides a feasible way for early strength and toughening modification of ce-mentitious composite pastes