Reviewerly/exHarmony huggingface dataset:
Online-compatible unsupervised nonresonant anomaly detection

There is a growing need for anomaly detection methods that can broaden the search for new particles in a model-agnostic manner. Most proposals for new methods focus exclusively on signal sensitivity. However, it is not enough to select anomalous events - there must also be a strategy to provide context to the selected events. We propose the first complete strategy for unsupervised detection of non-resonant anomalies that includes both signal sensitivity and a data-driven method for background estimation. Our technique is built out of two simultaneously-trained autoencoders that are forced to be decorrelated from each other. This method can be deployed offline for non-resonant anomaly detection and is also the first complete online-compatible anomaly detection strategy. We show that our method achieves excellent performance on a variety of signals prepared for the ADC2021 data challenge.
Robust Privacy-Preserving Motion Detection and Object Tracking in Encrypted Streaming Video

Video privacy leakage is becoming an increasingly severe public problem, especially in cloud-based video surveillance systems. It leads to the new need for secure cloud-based video applications, where the video is encrypted for privacy protection. Despite some methods that have been proposed for encrypted video moving object detection and tracking, none has robust performance against complex and dynamic scenes. In this paper, we propose an efficient and robust privacy-preserving motion detection and multiple object tracking scheme for encrypted surveillance video bitstreams. By analyzing the properties of the video codec and format-compliant encryption schemes, we propose a new compressed-domain feature to capture motion information in complex surveillance scenarios. Based on this feature, we design an adaptive clustering algorithm for moving object segmentation with an accuracy of 4×4 pixels. We then propose a multiple object tracking scheme that uses Kalman filter estimation and adaptive measurement refinement. The proposed scheme does not require video decryption or full decompression and has a very low computation load. The experimental results demonstrate that our scheme achieves the best detection and tracking performance compared with existing works in the encrypted and compressed domain. Our scheme can be effectively used in complex surveillance scenarios with different challenges, such as camera movement/jitter, dynamic background, and shadows.
H2Det: A High-Speed and High-Accurate Ship Detector in SAR Images

Synthetic aperture radar (SAR) sensor is a vital platform for ship detection whose accuracy and speed are usually difficult to balance.An urgent problem to be solved is how to achieve high-speed detection while maintaining high-accurate.To address this problem, we propose a high-speed and high-accurate detector (H2Det) in SAR images.For one thing, we adopt fewer convolutional layers, CSP module and rectangle filling to ensure model high-speed.For another, we propose spatial pyramid pooling (SPP), bottom-up path augmentation (PA), and mosaic data augmentation to ensure model high-accurate.To establish an optimal H2Det, we conduct comparative studies on SSDD dataset.Moreover, we verify the effectiveness of these modules mentioned above through ablation studies.The experimental results on SAR ship detection dataset (SSDD) demonstrate that both accuracy and speed of the proposed method outperform other state-of-the-art methods and references.In addition, the strong migration ability of the proposed H2Det is shown on high-resolution SAR images dataset (HRSID).
Privacy-Preserving Federated Learning for Industrial Edge Computing via Hybrid Differential Privacy and Adaptive Compression

With the continuous improvement of hardware computing power, edge computing of industrial data has been gradually applied. In the past decade, the promotion of edge computing has also greatly improved the efficiency of industrial production. Compared with the conventional cloud computing, it not only saves the bandwidth consumption of data transmission, but also ensures the terminal data security to a certain extent. However, the continuous update of attack types also put forward new requirements for the privacy protection of industrial edge computing. So it should fundamentally solve the risk of industrial data leakage in the process of deep model training in edge terminal. In this article, we propose a new federated edge learning framework based on hybrid differential privacy and adaptive compression for industrial data processing. Specifically, it first completes the adaptive gradient compression preparation, then constructs the industrial federated learning model, and finally makes use of adaptive differential privacy model to optimize, so as to complete the privacy protection towards the transmission of gradient parameters in industrial environment. By optimizing the hybrid differential privacy and adaptive compression, we can better prevent the terminal data privacy against inference attacks. The experimental results show that this method is very effective in the industrial edge computing situation, and it also opens up a new direction for the effect of differential privacy in federated learning.
Leveraging Batch Normalization for Vision Transformers

Transformer-based vision architectures have attracted great attention because of the strong performance over the convolutional neural networks (CNNs). Inherited from the NLP tasks, the architectures take Layer Normalization (LN) as a default normalization technique. On the other side, previous vision models, i.e., CNNs, treat Batch Normalization (BN) as a de facto standard, with the merits of faster inference than other normalization layers due to an avoidance of calculating the mean and variance statistics during inference, as well as better regularization effects during training.In this paper, we aim to introduce Batch Normalization to Transformer-based vision architectures. Our initial exploration reveals frequent crashes in model training when directly replacing all LN layers with BN, contributing to the un-normalized feed forward network (FFN) blocks. We therefore propose to add a BN layer in-between the two linear layers in the FFN block where stabilized training statistics are observed, resulting in a pure BN-based architecture. Our experiments proved that our resulting approach is as effective as the LN-based counterpart and is about 20% faster.
Semi-supervised robust training with generalized perturbed neighborhood

Adversarial examples have been shown to be a severe threat to deep neural networks (DNNs). One of the most effective adversarial defense methods is adversarial training (AT) through minimizing the adversarial risk Radv, which encourages both the benign example x and its adversarially perturbed neighborhoods within the ℓp-ball to be predicted as the ground-truth label. In this paper, we propose a novel defense method, the robust training (RT), by jointly minimizing two separated risks (i.e., Rstand and Rrob), which are with respect to the benign example and its neighborhoods, respectively. The motivation is to explicitly and jointly enhance the accuracy and the adversarial robustness. We prove that Radv is upper-bounded by Rstand+Rrob, which implies that RT has similar effect as AT. Intuitively, minimizing the standard risk enforces the benign example to be correctly predicted, while the robust risk minimization encourages the predictions of the neighbor examples to be consistent with the prediction of the benign example. Besides, since Rrob is independent of the ground-truth label, RT is naturally extended to the semi-supervised mode (i.e., SRT), to further enhance its effectiveness. Moreover, we extend the ℓp-bounded neighborhood to a general case, which covers different types of perturbations, such as the pixel-wise (i.e., x+δ) or the spatial perturbation (i.e., Ax+b). Extensive experiments on benchmark datasets not only verify the superiority of the proposed SRT to state-of-the-art methods for defending pixel-wise or spatial perturbations separately but also demonstrate its robustness to both perturbations simultaneously. Our work may shed the light on the understanding of universal model robustness and the potential of unlabeled samples. The code for reproducing main results is available at https://github.com/THUYimingLi/Semi-supervised_Robust_Training.
The Beijing Sentence Corpus: A Chinese sentence corpus with eye movement data and predictability norms

This report introduces the Beijing Sentence Corpus (BSC). This is a Chinese sentence corpus of eye-tracking data with relatively clear word boundaries. In addition, we report predictability norms for each word in the corpus. Eye movement corpora are available in alphabetic scripts such as English, German, and French. However, there is no publicly available corpus for Chinese. Thus, to study predictive processes during reading in Chinese, it is necessary to establish such a corpus. Also, given the clear word boundaries in the sentences, BSC is especially useful to provide evidence relevant to the theoretical debate of saccade target selection in Chinese. With the large-scale predictability norms, we conducted new analyses based on 60 BSC readers, testing the influences of launch word and target word properties while controlling for visual and oculomotor constraints, as well as sentence and subject-level individual differences. We discuss implications for guidance of eye movements in Chinese reading.
Learning to Detect Instance-Level Salient Objects Using Complementary Image Labels

Existing salient instance detection (SID) methods typically learn from pixel-level annotated datasets. In this paper, we present the first weakly-supervised approach to the SID problem. Although weak supervision has been considered in general saliency detection, it is mainly based on using class labels for object localization. However, it is non-trivial to use only class labels to learn instance-aware saliency information, as salient instances with high semantic affinities may not be easily separated by the labels. As the subitizing information provides an instant judgement on the number of salient items, it is naturally related to detecting salient instances and may help separate instances of the same class while grouping different parts of the same instance. Inspired by this observation, we propose to use class and subitizing labels as weak supervision for the SID problem. We propose a novel weakly-supervised network with three branches: a Saliency Detection Branch leveraging class consistency information to locate candidate objects; a Boundary Detection Branch exploiting class discrepancy information to delineate object boundaries; and a Centroid Detection Branch using subitizing information to detect salient instance centroids. This complementary information is then fused to produce a salient instance map. To facilitate the learning process, we further propose a progressive training scheme to reduce label noise and the corresponding noise learned by the model, via reciprocating the model with progressive salient instance prediction and model refreshing. Our extensive evaluations show that the proposed method plays favorably against carefully designed baseline methods adapted from related tasks.
Learning Data Streams With Changing Distributions and Temporal Dependency

In a data stream, concept drift refers to unpredictable distribution changes over time, which violates the identical-distribution assumption required by conventional machine learning methods. Current concept drift adaptation techniques mostly focus on a data stream with changing distributions. However, since each variable of a data stream is a time series, these variables normally have temporal dependency problems in the real world. How to solve concept drift and temporal dependency problems at the same time is rarely discussed in the concept-drift literature. To solve this situation, this article proves and validates that the testing error decreases faster if a predictor is trained on a temporally reconstructed space when drift occurs. Based on this theory, a novel drift adaptation regression (DAR) framework is designed to predict the label variable for data streams with concept drift and temporal dependency. A new statistic called local drift degree (LDD+) is proposed and used as a drift adaptation technique in the DAR framework to discard outdated instances in a timely way, thereby guaranteeing that the most relevant instances will be selected during the training process. The performance of DAR is demonstrated by a set of experimental evaluations on both synthetic data and real-world data streams.
Privacy Preservation in Online Social Networks Using Multiple-Graph-Properties-Based Clustering to Ensure k-Anonymity, l-Diversity, and t-Closeness

As per recent progress, online social network (OSN) users have grown tremendously worldwide, especially in the wake of the COVID-19 pandemic. Today, OSNs have become a core part of many people’s daily lifestyles. Therefore, increasing dependency on OSNs encourages privacy requirements to protect users from malicious sources. OSNs contain sensitive information about each end user that intruders may try to leak for commercial or non-commercial purposes. Therefore, ensuring different levels of privacy is a vital requirement for OSNs. Various privacy preservation methods have been introduced recently at the user and network levels, but ensuring k-anonymity and higher privacy model requirements such as l-diversity and t-closeness in OSNs is still a research challenge. This study proposes a novel method that effectively anonymizes OSNs using multiple-graph-properties-based clustering. The clustering method introduces the goal of achieving privacy of edge, node, and user attributes in the OSN graph. This clustering approach proposes to ensure k-anonymity, l-diversity, and t-closeness in each cluster of the proposed model. We first design the data normalization algorithm to preprocess and enhance the quality of raw OSN data. Then, we divide the OSN data into different clusters using multiple graph properties to satisfy the k-anonymization. Furthermore, the clusters ensure improved k-anonymization by a novel one-pass anonymization algorithm to address l-diversity and t-closeness privacy requirements. We evaluate the performance of the proposed method with state-of-the-art methods using a “Yelp real-world dataset”. The proposed method ensures high-level privacy preservation compared to state-of-the-art methods using privacy metrics such as anonymization degree, information loss, and execution time.
IMIIN: An inter-modality information interaction network for 3D multi-modal breast tumor segmentation

Breast tumor segmentation is critical to the diagnosis and treatment of breast cancer. In clinical breast cancer analysis, experts often examine multi-modal images since such images provide abundant complementary information on tumor morphology. Known multi-modal breast tumor segmentation methods extracted 2D tumor features and used information from one modal to assist another. However, these methods were not conducive to fusing multi-modal information efficiently, or may even fuse interference information, due to the lack of effective information interaction management between different modalities. Besides, these methods did not consider the effect of small tumor characteristics on the segmentation results. In this paper, We propose a new inter-modality information interaction network to segment breast tumors in 3D multi-modal MRI. Our network employs a hierarchical structure to extract local information of small tumors, which facilitates precise segmentation of tumor boundaries. Under this structure, we present a 3D tiny object segmentation network based on DenseVoxNet to preserve the boundary details of the segmented tumors (especially for small tumors). Further, we introduce a bi-directional request-supply information interaction module between different modalities so that each modal can request helpful auxiliary information according to its own needs. Experiments on a clinical 3D multi-modal MRI breast tumor dataset show that our new 3D IMIIN is superior to state-of-the-art methods and attains better segmentation results, suggesting that our new method has a good clinical application prospect.
360MonoDepth: High-Resolution 360° Monocular Depth Estimation

360° cameras can capture complete environments in a single shot, which makes 360° imagery alluring in many computer vision tasks. However, monocular depth estimation remains a challenge for 360° data, particularly for high resolutions like 2K (2048 × 1 024) and beyond that are important for novel-view synthesis and virtual reality applications. Current CNN-based methods do not support such high resolutions due to limited GPU memory. In this work, we propose aflexible framework for monocular depth estimation from high-resolution 360° images using tangent images. We project the 360° input image onto a set of tangent planes that produce perspective views, which are suitable for the latest, most accurate state-of-the-art perspective monocular depth estimators. To achieve globally consistent disparity estimates, we recombine the individual depth estimates using deformable multi-scale alignment followed by gradient-domain blending. The result is a dense, high-resolution 360° depth map with a high level of detail, also for outdoor scenes which are not supported by existing methods. Our source code and data are available at https://manurare.github.io/360monodepth/.
Design and Analysis of a Hybrid GNN-ZNN Model With a Fuzzy Adaptive Factor for Matrix Inversion

Motivated from the convergence capability achieved by gradient neural network (GNN) and zeroing neural network (ZNN) for matrix inversion, in this article, a novel hybrid GNN-ZNN (H-GNN-ZNN) model is proposed by introducing a fuzzy adaptive control strategy to generate a fuzzy adaptive factor that can change its size adaptively according to the residual error. Due to its fuzzy adaptability, this novel model is called the fuzzy adaptive GNN-ZNN (FA-GNN-ZNN) model for presentation convenience. We prove that the FA-GNN-ZNN model has the better performance than the existing H-GNN-ZNN model under the same conditions. In addition, different activation functions are applied to the FA-GNN-ZNN model to improve its performance further, and the corresponding theoretical analysis is given. Finally, comparative simulation results demonstrate the validity and superiority of the FA-GNN-ZNN model for matrix inversion.
Constrained evolutionary optimization based on reinforcement learning using the objective function and constraints

Solving constrained optimization problems (COPs) with evolutionary algorithms is highly active in the evolutionary computation community. Combining evolutionary algorithms with the learning techniques is an efficient way to obtain promising performance for the COPs. Based on this consideration, we propose a differential evolution assisted by reinforcement learning (RL), namely RL-CORCO, to effectively solve the COPs. The proposed method can be featured as (i) the Q-learning in RL is used for adaptive operator selection; (ii) the hierarchical population is set as a state to find the feasible optimal solution; and (iii) the correlation between constraints and the objective function is utilized. The RL-CORCO is tested on 18 benchmark problems in the CEC 2010 competition and 28 benchmark problems in the CEC 2017 competition. Experimental results show that in CEC2017, RL-CORCO performed better than others on 12 problems in 50 dimensions and 14 problems in 100 dimensions. The results of the Friedman’s test demonstrate the efficacy of the algorithm, which is able to obtain highly competitive results compared with other related methods. • Q-learning assisted DE is proposed for COPs. • Hierarchical population is set as state to find the optimal solution. • Correlation between constraints and objective function is utilized. • Experimental results demonstrate the efficacy of the algorithm.
A multimodal optical approach for investigating 3D-printed carbon PEEK composites

In the industrial field, it is of great interest to identify fast and low-cost manufacturing procedures to guarantee reliable and functional products. In this frame, 3D printing is a fast and low-cost innovative technique and its capability to produce fibre-reinforced polymer matrix composites widely opens the application possibilities. 3D printing technology can form also composite materials made by short fibres that can be exploited for manufacturing parts and structures in high-tech sectors such as aerospace, automotive or naval, to cite some. Here we show how a multimodal approach can furnish a whole characterisation of reinforced composites produced with a 3D printing technology during and after tensile and flexural tests. In the multimodal strategy, we employed a couple of full-field and non-contact optical techniques, i.e. 2D-Digital-Image-Correlation (2D-DIC) and Electronic Speckle Pattern Interferometry (ESPI) in combination with SEM and optical microscope for analysing the sample at the micro and macroscale. The result of the work allows us to achieve a complete characterisation of the components realized by 3D printing.
Effects of the vibrating graphene membrane and the driven classical field on an atomic system coupled to a cavity field

This paper studies the dynamic effects of the driven external classical field on a system consists of a single two-level atom that interacted locally with a vibrating graphene membrane and a cavity field. The temporal wave function containing the tripartite interaction statuses via a canonical transformation and new resonance conditions is obtained. The possibility of studying the optimal behaviour of the quantum coherence and non-classicality for the graphene-field, atom-field, and atom-graphene states are investigated. It is found that the upper and lower bounds of the two phenomena depend on the detuning parameter, coupling strength of atom-graphene interaction, and initial atomic state. Based on the support of numerical computations, we demonstrate how to control the quantum system by the vibrating graphene membrane and the driven classical field.
Oriented Ship Detection Based on Strong Scattering Points Network in Large-Scale SAR Images

Ship detection has broad applications in many areas, including fishery management, maritime rescue, and maritime monitoring. Recently, numerous detectors based on deep learning have been carried in ship detection in synthetic aperture radar (SAR) images. However, detecting the inshore ships faces enormous challenges because of the strong scattering interference of the inland area. In order to address such issues, a novel method named strong scattering points network for ship detection is proposed in this article. First, according to the SAR imaging mechanism, the ships usually appear strong scattering phenomenon in the SAR images. Therefore, the proposed method detects the strong scattering points on the ship and then aggregates their positions to obtain the ship’s arbitrary orientation box. Second, our method designs an embedding vector to cluster these points as an individual object to regress the oriented bounding box. Third, in order to distinguish the strong scattering points on land, a ship attention module is employed to extract the image texture features and representations of local features. It can suppress the false alarm caused by land interference in the detection process. Furthermore, to demonstrate the effectiveness of the proposed algorithm, this article introduces a new ship dataset for oriented ship detection named large-scale dataset for ship detection in SAR images (LDSD). Moreover, the public SAR ship detection dataset (SSDD) is utilized to verify the robustness and generalization ability of the detector. The experimental results on two datasets show that our method has a strong anti-interference ability in the inshore background and achieves state-of-the-art detection performance.