Reviewerly/exHarmony huggingface dataset:
Pseudo loss active learning for deep visual tracking

In visual tracking tasks, the training data are commonly composed of a large number of video sequences and each frame in the sequences needs to be labeled manually, which is labor-intensive and time-consuming. In addition, considering the similarity among the consecutive frames in the same sequence, there is significant redundancy in the training data. To address these problems, a novel pseudo loss active learning (PLAL) method is developed in this paper. PLAL aims to select the most informative and least redundant data for training to reduce the cost of labeling and maintain competitive tracking results simultaneously. Firstly, the Gaussian distribution based pseudo label is generated for the unlabeled candidates based on the tracking model which is initially trained on a small amount of training data. Then, the pseudo loss based on cross entropy is designed to compute the difference between the pseudo label and the target response map. The pseudo loss measures the uncertainty of the target spatial context which is used as the informativeness criterion of the image frame for selection. Meanwhile, a sampling interval threshold and a temporal penalty are employed for frame selection to avoid drastic variation in target appearance and reduce the redundancy within the consecutive candidate frames. Only the selected frames are labeled by the oracle (human expert) and then added to the training data. Extensive experiments on public benchmarks (OTB2013, OTB2015, VOT2018, UAV123, GOT-10K, TrackingNet, LaSOT, OxUvA and TLP) demonstrate that PLAL method outperforms the baseline and other recent active learning approaches. With only 3% of labeled data from the training dataset, PLAL reaches competitive performance (98-100%) compared to the model trained on the entire training dataset.
Double Sparse Deep Reinforcement Learning via Multilayer Sparse Coding and Nonconvex Regularized Pruning

Deep reinforcement learning (DRL), which highly depends on the data representation, has shown its potential in many practical decision-making problems. However, the process of acquiring representations in DRL is easily affected by interference from models, and moreover leaves unnecessary parameters, leading to control performance reduction. In this article, we propose a double sparse DRL via multilayer sparse coding and nonconvex regularized pruning. To alleviate interference in DRL, we propose a multilayer sparse-coding-structural network to obtain deep sparse representation for control in reinforcement learning. Furthermore, we employ a nonconvex log regularizer to promote strong sparsity, efficiently removing the unnecessary weights with a regularizer-based pruning scheme. Hence, a double sparse DRL algorithm is developed, which can not only learn deep sparse representation to reduce the interference but also remove redundant weights while keeping the robust performance. The experimental results in five benchmark environments of the deep q network (DQN) architecture demonstrate that the proposed method with deep sparse representations from the multilayer sparse-coding structure can outperform existing sparse-coding-based DRL in control, for example, completing Mountain Car with 140.81 steps, achieving near 10% reward increase from the single-layer sparse-coding DRL algorithm, and obtaining 286.08 scores in Catcher, which are over two times the rewards of the other algorithms. Moreover, the proposed algorithm can reduce over 80% parameters while keeping performance improvements from deep sparse representations.
PatchRNN: A Deep Learning-Based System for Security Patch Identification

With the increasing usage of open-source software (OSS) components, vulnerabilities embedded within them are propagated to a huge number of underlying applications. In practice, the timely application of security patches in downstream software is challenging. The main reason is that such patches do not explicitly indicate their security impacts in the documentation, which would be difficult to recognize for software maintainers and users. However, attackers can still identify these "secret" security patches by analyzing the source code and generate corresponding exploits to compromise not only unpatched versions of the current software, but also other similar software packages that may contain the same vulnerability due to code cloning or similar design/implementation logic. Therefore, it is critical to identify these secret security patches to enable timely fixes. To this end, we propose a deep learning-based defense system called PatchRNN to automatically identify secret security patches in OSS. Besides considering descriptive keywords in the commit message (i.e., at the text level), we leverage both syntactic and semantic features at the source-code level. To evaluate the performance of our system, we apply it on a large-scale real-world patch dataset and conduct a case study on a popular open-source web server software - NGINX. Experimental results show that the PatchRNN can successfully detect secret security patches with a low false positive rate.
Improvement of YOLOV5 Model Based on the Structure of Multiscale Domain Adaptive Network for Crowdscape

In this paper, we propose an improved model DAN-YOLOV5 based on YOLOV5. First, we use a mosaic enhancement strategy, which creates a large number of new samples on the existing VOC2007 dataset. Second, an innovative adaptive network module DAN is used on top of YOLOV5. The adaptive network module DAN is used to fuse features from same-layer scenes or cross-layer scenes. Finally, the experimental results show that the accuracy of the YOLOV5 dataset enhanced with shear-mixing and mosaic enhancement strategies is 71.02%, which is 13.56% better than the unenhanced data, and the average accuracy Figure is 80.05%, which is 33.11 percentage points better than the data. Applying the adaptive network module DAN to the YOLOV5 model, it improves the accuracy by 2.61% relative to YOLOV5 at 75.28%. Achieving such experimental results without increasing the computational effort and complexity at the grassroots level is well worth studying.
Label-aware graph representation learning for multi-label image classification

Multi-label image classification (MLIC) is a quintessential but challenging issue in the field of Computer Vision. Since the label co-occurrence is a crucial component of MLIC, previous existing approaches resort to the label co-occurrence for either modeling label correlations or modeling visual feature relationships. However, these methods ignore either the feature interaction or the label characteristics in MLIC. In this paper, we propose a label-aware graph representation learning (LGR) for MLIC that can explore the label interaction via a graph neural network built on the label co-occurrence and mine the feature correlations via another graph neural network also based on the label co-occurrence. Moreover, to decouple semantic visual features, current approaches resort to the word embedding guided semantic decoupling methods. However, the word embedding cannot clearly represent the label semantic information of MLIC. Hence, we reconstruct the semantic decoupling method by using the graph label representation. Extensive experiments on three benchmark datasets well demonstrate that our proposed framework can significantly achieve the state-of-the-art performance. In addition, a series of ablative studies further demonstrate the positive impacts of our proposed model.
Comparative analysis of machine learning models for anomaly detection in manufacturing

The introduction of various technologies in the context of Industry 4.0 allowed collecting monitoring data for various fields in manufacturing. Shop-floor and production data can be used for further analysis to extract knowledge. In this paper, an extensive evaluation of ten Machine Learning (ML) models for anomaly detection in manufacturing is conducted. The evaluation is conducted on multiple distinct ML algorithms, including conventional ML and a representative of Deep Neural Network (DNN) based algorithms. The ML models are trained on real production schedules to detect anomalous behavior in the overall system efficiency as well as violations in the delivery date of jobs. Multiple combinations of relevant features are tested during the training of the models. In essence, the objective of the ML models is to detect anomalous unknown breakdowns in the machines that lead to disruption in the overall performance of the system. The evaluation of the ML models is conducted on independent datasets with artificially injected machine breakdowns to establish the ground truth. These schedules with machine breakdowns are obtained through a simulation model. The results point to the high performance of a conventional ML algorithm, KNN. The performance demonstrated by a DNN-based AutoEncoder, conventional Local Outlier Factor-based Feature Bagging, and a recent Copula-Based Outlier Detection (COPOD) algorithms suggest limited applicability, which strongly depends on the shape of the data.
IFL-GAN: Improved Federated Learning Generative Adversarial Network With Maximum Mean Discrepancy Model Aggregation

The generative adversarial network (GAN) is usually built from the centralized, independent identically distributed (i.i.d.) training data to generate realistic-like instances. In real-world applications, however, the data may be distributed over multiple clients and hard to be gathered due to bandwidth, departmental coordination, or storage concerns. Although existing works, such as federated learning GAN (FL-GAN), adopt different distributed strategies to train GAN models, there are still limitations when data are distributed in a non-i.i.d. manner. These studies suffer from convergence difficulty, producing generated data with low quality. Fortunately, we found that these challenges are often due to the use of a federated averaging strategy to aggregate local GAN models' updates. In this article, we propose an alternative approach to tackling this problem, which learns a globally shared GAN model by aggregating locally trained generators' updates with maximum mean discrepancy (MMD). In this way, we term our approach improved FL-GAN (IFL-GAN). The MMD score helps each local GAN hold different weights, making the global GAN in IFL-GAN getting converged more rapidly than federated averaging. Extensive experiments on MNIST, CIFAR10, and SVHN datasets demonstrate the significant improvement of our IFL-GAN in both achieving the highest inception score and producing high-quality instances.
Privacy-Preserving Fog Aggregation of Smart Grid Data Using Dynamic Differentially-Private Data Perturbation

The edge of the smart grid has a massive number of power and resource-constrained interconnected devices. Mainly, smart meters report power consumption data from consumer homes, industrial buildings, and other connected infrastructures. Multiple approaches were proposed in the literature to preserve the privacy of consumers by altering the data via additive noise, masking, or other data obfuscation techniques. A significant body of work in the literature employs differential privacy methods with constraining predefined parameters to achieve the optimal trade-off between privacy and utility of the data. However, billing accuracy can be degraded by using such additive noise techniques. We propose a differentially-private model that perturbs data by adding noise obtained from a virtual chargeable battery, while maintaining billing accuracy. Our model utilizes fog-computing data aggregation with lightweight cryptographic primitives to ensure the authenticity and confidentiality of data generated by low-end devices. We describe our differentially-private model with flexible constraints and a dynamic window algorithm to maintain the privacy-budget loss in infinitely generated time-series data. Our experimental results show a possible decrease in data perturbation error by 51.7% and 61.2% for smart meters and fog-computing data aggregators perturbed data, respectively, compared to the commonly used Gaussian mechanism.
Resilient Filtering of Nonlinear Complex Dynamical Networks Under Randomly Occurring Faults and Hybrid Cyber-Attacks

This paper investigates the problem of recursive resilient filtering for discrete-time nonlinear dynamical networks with randomly switching topologies under randomly occurring faults and hybrid cyber-attacks. The attacks considered are composed of DoS attacks describing by binary random sequences and deception attacks, the amplitude of which obeys the Gaussian mixing distribution. A recursive resilient filter dependent on DoS attack sequences is designed to simultaneously estimate system states and occurred faults, where the desired gain is parameterized via the matrix iterative formula of filtering error covariance. Furthermore, the maximum posterior probability estimation is calculated to identify DoS attack sequences with the help of the classical Bayesian rule. In particular, a modified recursive filtering algorithm is established by replacing the corresponding parameters in the previously derived formulas about filter gains and covariance matrices. Finally, the effectiveness of the proposed recursive filtering algorithm is demonstrated by a simulation example.
SimViT: Exploring a Simple Vision Transformer with Sliding Windows

Although vision Transformers have achieved excellent performance as backbone models in many vision tasks, most of them intend to capture global relations of all tokens in an image or a window, which disrupts the inherent spatial and local correlations between patches in 2D structure. In this paper, we introduce a simple vision Transformer named SimViT, to incorporate spatial structure and local information into the vision Transformers. Specifically, we introduce Multi-head Central Self-Attention(MCSA) instead of conventional Multi-head Self-Attention to capture highly local relations. The introduction of sliding windows facilitates the capture of spatial structure. Meanwhile, SimViT extracts multiscale hierarchical features from different layers for dense prediction tasks. Extensive experiments show the SimViT is effective and efficient as a general-purpose backbone model for various image processing tasks. Especially, our SimViT-Micro only needs 3.3M parameters to achieve 71.1% top-1 accuracy on ImageNet-1k dataset, which is the smallest size vision Transformer model by now.
Task Encoding With Distribution Calibration for Few-Shot Learning

Few-shot learning is an extremely challenging task in computer vision that has attracted increased research attention in recent years. However, most recent methods do not fully use the task’s information, and few of the seen samples result in large intraclass differences among the same classes. In this paper, we propose a novel task encoding with distribution calibration (TEDC) model for few-shot learning, which uses the relationships among the feature distributions to reduce intraclass differences. In the TEDC model, an integrated feature extraction module (IFEM) is proposed, which extracts the multiangle visual features of an image and fuses them to obtain more representative features. To effectively utilize the task information, a novel task encoding module (TEM) is proposed, which obtains the task features by fusing all the seen samples’ information and uses them to adjust all the samples’ features for more generalizable task-specific representations. We also propose a distribution calibration module (DCM) to reduce the bias between the distribution of the support features and the query features in the same class. Extensive experiments show that our proposed TEDC model achieves an excellent performance and outperforms the state-of-the-art methods on three widely used few-shot classification benchmarks, specifically miniImageNet, tieredImageNet and CUB-200-2011.
Blind False Data Injection Attacks Against State Estimation Based on Matrix Reconstruction

Cyber, especially false data injection attacks (FDIAs), are gradually becoming a kind of severe threat to modern power grids. This paper proposes a blind FDIA approach against the state estimation of power grids based on matrix reconstruction and subspace estimation. As a blind FDIA method, it requires no information about the system parameters and topology, but only needs a limited period of measurement data. In particular, this paper investigates the interference of random measurement noise for subspace based blind FDIA methods, and designs a scheme to alleviate the influence of this random noise in the process of FDIA. Compared with previous blind FDIA methods, the proposed method overcomes the shortcoming that measurement noise is difficult to address when data is not abundant. Thus the presented approach performs high successful rate of FDIA, and it is able to operate when measurement data is very limited. Besides, it performs great robustness to large-scale power grids and high-level measurement noise. Numerous cases demonstrate the effectiveness and advantages of the proposed method.
Ultrafast spatial phase unwrapping algorithm with accurately correcting transient phase error

In fringe projection profilometry, the wrapped phase is easily polluted by many factors such as noise, shadow, and so on. In this Letter, we propose an ultrafast bi-staggered spatial phase unwrapping (BSPU) method. By constructing another staggered phase, the fringe order jump (FOJ) and local transient phase error (LTPE) can be accurately and quickly located at the same time owing to a simple difference operation. For the first time, to the best of our knowledge, a pioneering threshold separation model is established to precisely distinguish FOJ and LTPE. Based on the continuity assumption, LTPE is effectively corrected by introducing the concept of "non-integer fringe order." The range of measurable discontinuity height is improved owing to the distinction between real phase jump and random error in the spatial phase unwrapping. In addition, it is thousands of times faster than the traditional path-dependent algorithm and even has higher measurement accuracy. Experimental results show the effectiveness and robustness of the proposed method in various complex measurement environments.
A joint FrameNet and element focusing Sentence-BERT method of sentence similarity computation

As one of the fundamental research areas of natural language processing, sentence similarity computation attracts researchers' attention. Considering two single independent sentences, it is difficult to measure the similarity between them without sufficient context information. To solve this issue, we propose a joint FrameNet and element focusing Sentence-BERT method of sentence similarity computation (FEFS3C). Considering the actual meaning of sentences, we adopt the frame semantics theory and adapt FrameNet in FEFS3C. Moreover, focusing on critical information conveyed in sentences, FEFS3C takes the superiority of deep learning technologies and proposes a new sentence representation model element focusing Sentence-BERT (EF-SBERT) which improves traditional sentence representations. Two primary considerations of sentences in FEFS3C "sentence meaning" and "critical sentence information" aim to better utilize the influence of sentences context. To evaluate the performance of FEFS3C, we carried out experiments on the standard test set "STS-B". Results show that FEFS3C has obtained better Spearman correlation compared with traditional methods.
Automatic Requirements Classification Based on Graph Attention Network

Requirements classification is a significant task for requirements engineering, which is time-consuming and challenging. The traditional requirements classification models usually rely on manual pre-processing and have poor generalization capability. Moreover, these traditional models ignore the sentence structure and syntactic information in requirements. To address these problems, we propose an automatic requirements classification based BERT and graph attention network (GAT), called DBGAT. We construct dependency parse trees and then utilize the GAT for mining the implicit structure feature and syntactic feature of requirements. In addition, we introduce BERT to improve the generalization ability of the model. Experimental results of the PROMISE datasets demonstrate that our proposed DBGAT significantly outperforms existing state-of-the-art methods. Moreover, we investigate the impact of graph construction methods on non-functional requirements classification. DBGAT achieved the best classification results on both seen (F1-scores of up to 91&#x0025;) and unseen projects (F1-scores of up to 88&#x0025;), further demonstrating the strong generalization ability.
Towards Disentangling Latent Space for Unsupervised Semantic Face Editing

Facial attributes in StyleGAN generated images are entangled in the latent space which makes it very difficult to independently control a specific attribute without affecting the others. Supervised attribute editing requires annotated training data which is difficult to obtain and limits the editable attributes to those with labels. Therefore, unsupervised attribute editing in an disentangled latent space is key to performing neat and versatile semantic face editing. In this paper, we present a new technique termed Structure-Texture Independent Architecture with Weight Decomposition and Orthogonal Regularization (STIA-WO) to disentangle the latent space for unsupervised semantic face editing. By applying STIA-WO to GAN, we have developed a StyleGAN termed STGAN-WO which performs weight decomposition through utilizing the style vector to construct a fully controllable weight matrix to regulate image synthesis, and employs orthogonal regularization to ensure each entry of the style vector only controls one independent feature matrix. To further disentangle the facial attributes, STGAN-WO introduces a structure-texture independent architecture which utilizes two independently and identically distributed (i.i.d.) latent vectors to control the synthesis of the texture and structure components in a disentangled way. Unsupervised semantic editing is achieved by moving the latent code in the coarse layers along its orthogonal directions to change texture related attributes or changing the latent code in the fine layers to manipulate structure related ones. We present experimental results which show that our new STGAN-WO can achieve better attribute editing than state of the art methods.
Coin Identification and Conversion System using Image Processing

Different features and template designs of currencies give travellers a hard time identifying and recognizing a coin; that is why a device to locate and recognize a coin is a must. Four currencies were used in the study. The researchers applied the YOLOv3 and CNN to create the system. Identification and recognition of coins is challenging with their many orientations and widely changing patterns. To resolve the problem, the researchers obtained the optimal distance of the camera to the coin and tested different angles of rotation. It was found out that a 6-cm distance from the camera to the coin is best for easy coin identification. The system’s accuracy are 98.15%, 98.15%, 97.22%, and 96.30% for 0-, 90-, 180-, and 270-degree angles, respectively.