Reviewerly/exHarmony huggingface dataset:
New Magnitude Proxy for Earthquake Early Warning Based on Initial Time Series and Frequency

Abstract A new characteristic parameter Sdτ is proposed to improve the performance of magnitude estimation in earthquake early warning (EEW). Sdτ is the product of summation of absolute displacement multiplied by the maximum predominant period (τmaxP) for the first arriving seconds of a seismic wave. About 30,725 underground records at borehole stations for 3645 earthquakes with magnitude between 4.0 and 9.0 from the Japanese KiK-net were used to compare the magnitude proxy performance based on the proposed Sdτ with that based on either τmaxP or peak displacement Pd. The comparison results show that for a magnitude between 4.0 and 7.3, Sdτ has a better correlation with magnitude and higher estimated accuracy than either τmaxP or Pd. Hypocentral distance is not required when using Sdτ, but it can be used to further improve the accuracy of magnitude estimate. These results confirm that Sdτ can significantly improve the accuracy and timeliness of continuous magnitude estimation in an EEW system.
Boundary and Context Aware Training for CIF-Based Non-Autoregressive End-to-End ASR

Continuous integrate-and-fire (CIF) based models, which use a soft and monotonic alignment mechanism, have been well applied in non-autoregressive (NAR) speech recognition with competitive performance compared with other NAR methods. However, such an alignment learning strategy may suffer from an erroneous acoustic boundary estimation, severely hindering the convergence speed as well as the system performance. In this paper, we propose a boundary and context aware training approach for CIF based NAR models. Firstly, the connectionist temporal classification (CTC) spike information is utilized to guide the learning of acoustic boundaries in the CIF. Besides, an additional contextual decoder is introduced behind the CIF decoder, aiming to capture the linguistic dependencies within a sentence. Finally, we adopt a recently proposed Conformer architecture to improve the capacity of acoustic modeling. Experiments on the open-source Mandarin AISHELL-1 corpus show that the proposed method achieves a comparable character error rates (CERs) of 4.9% with only 1/24 latency compared with a state-of-the-art autoregressive (AR) Conformer model. Futhermore, when evaluating on an internal 7500 hours Mandarin corpus, our model still outperforms other NAR methods and even reaches the AR Conformer model on a challenging real-world noisy test set.
Hiding Assistive Robots During Training in Immersive VR Does Not Affect Users’ Motivation, Presence, Embodiment, Performance, Nor Visual Attention

Combining immersive virtual reality (VR) using head-mounted displays (HMDs) with assisting robotic devices might be a promising procedure to enhance neurorehabilitation. However, it is still an open question how immersive virtual environments (VE) should be designed when interacting with rehabilitation robots. In conventional training, the robot is usually not visually represented in the VE, resulting in a visuo-haptic sensory conflict between what users see and feel. This study aimed to investigate how motivation, embodiment, and presence are affected by this visuo-haptic sensory conflict. Using an HMD and a rehabilitation robot, 28 healthy participants performed a path-tracing task, while the robot was either visually reproduced in the VE or not and while the robot either assisted the movements or not. Participants' performance and visual attention were measured during the tasks, and after each visibility/assistance condition, they reported their motivation, presence, and embodiment with questionnaires. We found that, independently of the assistance, the robot visibility did not affect participants' motivation, presence, embodiment, nor task performance. We only found a greater effort/importance reported when the robot was visible. The visual attention was also slightly affected by the robot's visibility. Importantly, we found that the robotic assistance hampered presence and embodiment, but improved motivation. Our results indicate no disadvantage of not reproducing robotic devices in VEs when using HMDs. However, caution must be put when developing assisting controllers, as they might hamper users' affect.
Few-Shot Relation Classification Research Based on Prototypical Network and Causal Intervention

To improve the accuracy of the few-shot relation classification task, the research, which weakens the influence of confounder on the performance of the model and enhances the semantic representation and feature extraction ability of the model, is carried out based on the prototypical network. And then, the weaken-confounders method based on causal intervention(WCCI) is proposed, and the RBERTI-Proto model is constructed. In WCCI, the pre-trained knowledge is stratified by the backdoor adjustment based on causal intervention, the optimal stratified number is determined by a stratified method, and the BN layer is introduced for the gradient disappearance problem. In the RBERTI-Proto model, the abilities of semantic representation and feature extraction of the model are enhanced by which the RoBERTa is used as the feature extractor of the model. Experimental results demonstrate the effectiveness of our proposed methods and the RoBERTa model as feature extractor of our model, and the ACC value of the RBERTI-Proto model achieve 93.38% on the 5-way 5-shot scenario of the FewRel dataset.
LOT: A Story-Centric Benchmark for Evaluating Chinese Long Text Understanding and Generation

Abstract Standard multi-task benchmarks are essential for developing pretraining models that can generalize to various downstream tasks. Existing benchmarks for natural language processing (NLP) usually focus only on understanding or generating short texts. However, long text modeling requires many distinct abilities in contrast to short texts, such as the modeling of long-range discourse and commonsense relations, and the coherence and controllability of generation. The lack of standardized benchmarks makes it difficult to assess these abilities of a model and fairly compare different models, especially Chinese models. Therefore, we propose a story-centric benchmark named LOT for evaluating Chinese long text modeling, which aggregates two understanding tasks and two generation tasks. We construct new datasets for these tasks based on human-written Chinese stories with hundreds of words. Furthermore, we release an encoder-decoder-based Chinese long text pretraining model named LongLM with up to 1 billion parameters. We pretrain LongLM on 120G Chinese novels with two generative tasks including text infilling and conditional continuation. Extensive experiments show that LongLM outperforms similar-sized pretraining models substantially on both the understanding and generation tasks in LOT.
Activation function design for deep networks: linearity and effective initialisation

The activation function deployed in a deep neural network has great influence on the performance of the network at initialisation, which in turn has implications for training. In this paper we study how to avoid two problems at initialisation identified in prior works: rapid convergence of pairwise input correlations, and vanishing and exploding gradients. We prove that both these problems can be avoided by choosing an activation function possessing a sufficiently large linear region around the origin, relative to the bias variance σb2 of the network's random initialisation. We demonstrate empirically that using such activation functions leads to tangible benefits in practice, both in terms of test and training accuracy and in terms of training time. Furthermore, we observe that the shape of the nonlinear activation outside the linear region appears to have a relatively limited impact on training. Finally, our results also allow us to train networks in a new hyperparameter regime, with a much larger bias variance than has previously been possible.
Unsupervised SAR and Optical Image Matching Using Siamese Domain Adaptation

Due to its highly complementary information about remote sensing, synthetic aperture radar (SAR) and optical imagery matching have drawn much attention in recent years. Compared with traditional methods, deep learning-based SAR-optical image matching models largely rely on supervision with ground truths, where the matching accuracy suffers because of unseen image domains. To mitigate loads in burdensome labeling tasks, transferring deep learning models trained with annotated source domains to nonannotated target domains has attracted great concern. Due to the domain gap, the difference between the source and target domains is likely to deteriorate the matching accuracy on target data if the training process is directly conducted without proper domain adaptation (DA). In this research, a Siamese DA (SDA) approach with a combined loss function is developed in the context of multimodality image matching. Then, a novel rotation/scale-invariant transformation module with regression modules is designed to extract rotation/scale-equivariant features. Finally, the causal inference-based self-learning method and the multiresolution histogram matching approach are employed to enhance the unsupervised matching performance. Experimental results on the RadarSat/Planet dataset and the Sentinel-1/2 dataset demonstrate that the developed model can achieve competitive matching performance with a low overlap ratio between domains and little data labeling. By alleviating the domain discrepancy, the developed model drastically reduces the average L2 score of the unsupervised matching from 9.576 to 0.658, while the less-than-one-pixel matching error rate is enhanced from 66.3% to 90.6%.
User Selection for Federated Learning in a Wireless Environment: A Process to Minimize the Negative Effect of Training Data Correlation and Improve Performance

This article describes our study about federated learning by using the front view-based training process in autonomous driving as an example. The overlap of camera views among users creates training data correlation. It is shown that this correlation undermines the optimization performance of the federated learning process. To reduce the negative effect of correlation within training data while limiting wireless channel resource utilization, a federated learning protocol with user selection is proposed. The protocol selects a limited number of users with sufficient spatial separation in each round of federated learning. An exclusion zone is applied to maintain separation during user selection. Experimental results show that in an example deployment scenario with a user density of 0.05, applying a discrete exclusion zone (DEZ) to prevent selecting the first three nearest users and applying a geographical exclusion zone (DEZ) to avoid selecting users within 70 m have equivalent effects on reducing training data. Furthermore, both methods can provide the performance of five-user federated learning approaches, an ideal case without correlation in the training data.
Instance-Level Knowledge Transfer for Data-Driven Driver Model Adaptation With Homogeneous Domains

Driver model adaptation (DMA) plays an essential role for driving behaviour modelling when there is a lack of sufficient data for training the new model. A new data-driven DMA method is proposed in this paper to realise the instance-level knowledge transfer between individual drivers. Using the importance-weighted transfer learning (IWTL), the data collected from one driver (source driver) can be directly used to train the model of another driver (target driver). Under the framework of IWTL, the relationship between two different drivers can be modelled by the importance weight (IW). Two estimation methods Kullback-Leibler (KL) Divergence and least-squares (LS), are used to estimate IW for each data instance by modelling the importance-weight function as a radial basis function (RBF). Experiments based on the driving simulator and real vehicle are carried out to test the performance of TL for steering behaviour adaptation during the overtaking manoeuvre. The experimental results show that the TL method can transfer the knowledge observed from one driver to another when training the new driver model without sufficient data by keeping the modelling error at a low level.
Towards automatic model specialization for edge video analytics

The number of cameras deployed to the edge of the network increases by the day, while emerging use cases, such as smart cities or autonomous driving, also grow to expect images to be analyzed in real-time by increasingly accurate and complex neural networks. Unfortunately, state-of-the-art accuracy comes at a computational cost rarely available in the edge cloud. At the same time, due to strict latency constraints and the vast amount of bandwidth edge cameras generate, we can no longer rely on offloading the task to a centralized cloud. Consequently, there is a need for a meeting point between the resource-constrained edge cloud and accurate real-time video analytics. If state-of-the-art models are too expensive to run on the edge, and lightweight models are not accurate enough for the use cases in the edge, one solution is to demand less from the lightweight model and specialize it in a narrower scope of the problem, a technique known as model specialization. By specializing a model to the context of a single camera, we can boost its accuracy while keeping its computational cost constant. However, this also involves one training per camera, which quickly becomes unfeasible unless the entire process is fully automated. In this paper, we present and evaluate COVA (Contextually Optimized Video Analytics), a framework to assist in the automatic specialization of models for video analytics in edge cloud cameras. COVA aims to automatically improve the accuracy of lightweight models by specializing them to the context to which they will be deployed. Moreover, we discuss and analyze each step involved in the process to understand the different trade-offs that each one entails. Using COVA, we demonstrate that the whole pipeline can be effectively automated by leveraging large neural networks used as teachers whose predictions are used to train and specialize lightweight neural networks. Results show that COVA can automatically improve pre-trained models by an average of 21% mAP on the different scenes of the VIRAT dataset.
Multi-UAV Objective Assignment Using Hungarian Fusion Genetic Algorithm

In the background of air combat, the situation between multiple unmanned aerial vehicle (multi-UAV) and objectives has a certain impact on the task assignment. In order to improve the efficiency of assignment and obtain the best assignment scheme during the process of performing tasks, this paper proposes a method to evaluate the situation at a certain time. This method is the basis for establishing a multi-UAV objective assignment model. For solving the model, this paper presents the Hungarian fusion Genetic Algorithm. It first uses the feasible solutions solved by the Hungarian algorithm as the elite individuals in the initial population of the genetic algorithm, and then uses the objective function in the assignment model as the fitness function to optimize the results. The algorithm solves the problem that the assignment result of the Hungarian algorithm is not unique, and optimizes the drawback that the traditional Genetic Algorithm is prone to fall into local optimum. The simulation verified the effectiveness of the situational assessment method and the improved algorithm.
Efficient Algorithms for Maximal k-Biplex Enumeration

Mining maximal subgraphs with cohesive structures from a bipartite graph has been widely studied. One important cohesive structure on bipartite graphs is k-biplex, where each vertex on one side disconnects at most k vertices on the other side. In this paper, we study the maximal k-biplex enumeration problem which enumerates all maximal k-biplexes. Existing methods suffer from efficiency and/or scalability issues and have the time of waiting for the next output exponential w.r.t. the size of the input bipartite graph (i.e., an exponential delay). In this paper, we adopt a reverse search framework called bTraversal, which corresponds to a depth-first search (DFS) procedure on an implicit solution graph on top of all maximal k-biplexes. We then develop a series of techniques for improving and implementing this framework including (1) carefully selecting an initial solution to start DFS, (2) pruning the vast majority of links from the solution graph of bTraversal, and (3) implementing abstract procedures of the framework. The resulting algorithm is called iTraversal, which has its underlying solution graph significantly sparser than (around 0.1% of) that of bTraversal. Besides, iTraversal provides a guarantee of polynomial delay. Our experimental results on real and synthetic graphs, where the largest one contains one billion edges, show that our algorithm is up to four orders of magnitude faster than existing algorithms.
ReDefender: A Tool for Detecting Reentrancy Vulnerabilities in Smart Contracts Effectively

Reentrancy, one of the most complex type of vulner-abilities, poses significant threat to smart contract development. Indeed, millions of dollars have evaporated due to reentrancy vulnerabilities of smart contracts in past years. In this paper, we propose a new approach to detect reentrancy vulnerabilities using fuzz testing and develop a novel tool named ReDefender. Our approach and tool consists of four main steps: (1)preprocess contract to be detected: when a contract uploaded, its source code will be preprocessed by ReDefender to extract candidate pool for fuzzing; (2) generate fuzzing input: fuzzing input will be generated by fuzz engine; (3) collect runtime information: an agent contract is constructed to interact with and attack all contracts to be detected. Runtime information is collected during the execution of every fuzzing input; (4) analyze execution log and find reentrancy: the execution log is analyzed to determine whether a malicious reentrancy occurs. We conduct experiments on 204 tagged smart contracts and show the higher accuracy and lower false positive rate of ReDefender than that of other three famous tools. Moreover, we conduct a new experiment and find 4 reentrancy vulnerabilities in 395 on-chain contract accounts which have managed more than 1000 transactions.
Responsive Listening Head Generation: A Benchmark Dataset and Baseline

We present a new listening head generation benchmark, for synthesizing responsive feedbacks of a listener (e.g., nod, smile) during a face-to-face conversation. As the indispensable complement to talking heads generation, listening head generation has seldomly been studied in literature. Automatically synthesizing listening behavior that actively responds to a talking head, is critical to applications such as digital human, virtual agents and social robots. In this work, we propose a novel dataset "ViCo", highlighting the listening head generation during a face-to-face conversation. A total number of 92 identities (67 speakers and 76 listeners) are involved in ViCo, featuring 483 clips in a paired "speaking-listening" pattern, where listeners show three listening styles based on their attitudes: positive, neutral, negative. Different from traditional speech-to-gesture or talking-head generation, listening head generation takes as input both the audio and visual signals from the speaker, and gives non-verbal feedbacks (e.g., head motions, facial expressions) in a real-time manner. Our dataset supports a wide range of applications such as human-to-human interaction, video-to-video translation, cross-modal understanding and generation. To encourage further research, we also release a listening head generation baseline, conditioning on different listening attitudes. Code & ViCo dataset: https://project.mhzhou.com/vico .
Phase arrival picking for bridging multi-source downhole microseismic data using deep transfer learning

Abstract The phase arrival picking of the downhole microseismic dataset is a critical step in fracturing monitoring data processing. Recently, data-driven methods have been widely used in seismology studies, especially in seismic phase picking. The picking results heavily depend on whether large quantities of accurately labeled phase samples could be obtained to extract the characteristics of seismic waveforms. Also, there is a shortcoming of poor generalization ability in dealing with the cross-source transfer scenarios. In this paper, we propose a novel deep transfer learning method for microseismic phase arrival picking by fine-tuning one existing pretrained model based on a few phase samples. The pretrained model, which has been domain-adapted for phase picking, adopts 2D U-Net to both extract time and space features, thereby improving the overall picking accuracy. Moreover, the fully convolutional U-Net architecture has the ability to handle samples with variable sizes so could be used for bridging downhole microseismic data from different sources. The results of two transfer cases show that compared with the direct application of the pretrained model and a newly trained model, the proposed method could provide more satisfactory performance with only limited seismic phase samples. Also, our method significantly reduces the cost of labeling and saves time because of avoiding repeated training.
A Lightweight and Verifiable Access Control Scheme With Constant Size Ciphertext in Edge-Computing-Assisted IoT

As an extension of cloud computing, edge computing has attracted the attention of academia and industry because of its characteristics of low latency, high bandwidth, and low energy consumption. However, due to limited terminal resources and insufficient security design, the edge computing environment still faces many challenges in terms of data security and privacy protection. Among them, how to effectively control access to outsourced data is one of the main issues. In this article, we propose a lightweight and verifiable ciphertext-policy attribute-based encryption (CP-ABE)-based multiauthority access control scheme for edge computing-assisted Internet of Things (IoT), which adopts the method of outsourcing decryption to mitigate the computational cost of data users with limited resources. In addition, our scheme realizes the feature of attribute revocation, and the design of the multiauthority mechanism enables our scheme to avoid the problem of key escrow. Therefore, our proposed scheme not only ensures data confidentiality but also can resist the collusion attack. Besides, our scheme is secure against the chosen plaintext attack in the random oracle model under the decision <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$q$ </tex-math></inline-formula> -BDHE assumption. Finally, we compared our scheme with some related work in performance, and the results demonstrate that our scheme is efficient in computation and communication. Because our scheme greatly mitigates the overhead of data users, it is very suitable for edge computing supported IoT applications with restricted computation resources.