Reviewerly/exHarmony huggingface dataset:
Oriented Ship Detector for Remote Sensing Imagery Based on Pairwise Branch Detection Head and SAR Feature Enhancement

Recently, object detection in natural images has made a breakthrough, but it is still challenging in oriented ship detection for remote sensing imagery. Considering some limitations in this task, such as uncertain ship orientation, unspecific features for locating and classification in the complex optical environment, and multiplicative speckle interference of synthetic aperture radar (SAR), we propose an oriented ship detector based on the pairwise branch detection head and adaptive SAR feature enhancement. The details are as follows: (1) Firstly, the ships with arbitrary directions are described with a rotated ground truth, and an oriented region proposal network (ORPN) is designed to study the transformation from the horizontal region of interest to the rotated region of interest. The ORPN effectively improved the quality of the candidate area while only introducing a few parameters. (2) In view of the existing algorithms that tend to perform classification and regression prediction on the same output feature, this paper proposes a pairwise detection head (PBH) to design parallel branches to decouple classification and locating tasks, so that each branch can learn more task-specific features. (3) Inspired by the ratio-of-average detector in traditional SAR image processing, the SAR edge enhancement (SEE) module is proposed, which adaptively enhances edge pixels, and the threshold of the edge is learned by the channel-shared adaptive thresholds block. Experiments were carried out on both optical and SAR datasets. In the optical dataset, PBH combined with ORPN improved recall by 5.03%, and in the SAR dataset, the overall method achieved a maximum F1 score improvement of 6.07%; these results imply the validity of our method.
Nonverbal Sound Detection for Disordered Speech

Voice assistants have become an essential tool for people with various disabilities because they enable complex phone-or tablet-based interactions without the need for fine-grained motor control, such as with touchscreens. However, these systems are not tuned for the unique characteristics of individuals with speech disorders, including many of those who have a motor-speech disorder, are deaf or hard of hearing, have a severe stutter, or are minimally verbal. We introduce an alternative voice-based input system which relies on sound event detection using fifteen nonverbal mouth sounds like "pop", "click", or "eh." This system was designed to work regardless of ones’ speech abilities and allows full access to existing technology. In this paper, we describe the design of a dataset, model considerations for real-world deployment, and efforts towards model personalization. Our fully-supervised model achieves segment-level precision and recall of 88.6% and 88.4% on an internal dataset of 710 adults, while achieving 0.31 false positives per hour on aggressors such as speech. Five-shot personalization enables satisfactory performance in 84.5% of cases where the generic model fails.
Transferable adversarial examples can efficiently fool topic models

Intelligent text systems have been shown vulnerable to adversarial attacks. A popular way to enhance the robustness is to leverage transferable adversarial examples. As one of the most important statistical models, topic model is widely used in various applications. However, whether adversarial examples exhibit good transferability across topic models has never been explored. In this paper, we propose an effective adversarial example generator TopicAttack to disturb the inference of a target victim topic model and an ensemble algorithm TopicAttack+ to further increase transferability. TopicAttack exploits the words of high importance and constructs adversarial examples via synonym transformation. TopicAttack+ searches the optimal model ensemble given a set of prescribed substitutes, then generate adversarial examples of stronger transferability. Numerically, we demonstrate the effectiveness of the proposed method on the benchmark datasets AP, NIPS and 20News. In particular, given eight benchmark substitute models, TopicAttack disturbs them all effectively by achieving high Kullback-Leibler (KL) divergence; and TopicAttack+ significantly improves the average transferability performance on NIPS from 1.308 to 1.695.
Balanced and Accurate Pseudo-Labels for Semi-Supervised Image Classification

Image classification by semi-supervised learning has recently become a hot spot, and the Co-Training framework is an important method of semi-supervised image classification. In the traditional Co-Training structure, the sub-networks will generate pseudo-labels for each other, and these pseudo-labels will further be used as a supervisory signal for model training. However, the pseudo-labels will hurt classification performance because of their low accuracy and unbalanced distribution. In this article, we are trying to solve the preceding two problems by designing the Balanced Module (BM) and Gaussian Mixture Module (GMM), and propose BAPS (the B alanced and A ccurate P seudo-labels for S emi-supervised image classification). In BM, the two sub-networks jointly predict the unlabeled images, then select the pseudo-labels with a high-confidence threshold to perform the balancing operation to obtain the initial samples with balanced distribution of each category. In GMM, referring to the common practice of the Learning from Noise Labels task, we use GMM to fit the loss distribution of images with pseudo-labels output by BM, then clean samples and noise samples are divided based on the observation that the loss of correctly labeled images is generally smaller than that of wrongly labeled ones. Through BM and GMM, pseudo-labels with balanced distribution and high accuracy are obtained for the subsequent model training process. Our model has achieved better classification accuracy than most state-of-the-art semi-supervised image classification algorithms on the CIFAR-10/100 and SVHN datasets, and further ablation experiments demonstrate the effectiveness of our BAPS. The source code of BAPS will be available at https://github.com/zhaojianaaa .
Group-Wise Feature Selection for Supervised Learning

Feature selection has been explored in two ways, global feature selection and instance-wise feature selection. Global feature selection picks the same feature selector for the entire dataset, while instance-wise feature selection allows different feature selectors for different data instances. We propose group-wise feature selection, a new setting that sits between global and instance-wise feature selections. In group-wise feature selection, we constrain the number of possible feature selectors to be a finite number K, which allows different feature selectors while regularizing the number of different selectors. This is for flexible trade-offs between expressiveness and model complexity. We propose two techniques to solve the problem: the first applies K-Means Clustering to the instance-wise feature selection algorithm; the second uses the mixture of experts model with Gumbel-Softmax to learn group membership and feature selector simultaneously. We evaluate our techniques and show promising results on both synthetic and real datasets.
Call-Sign Recognition and Understanding for Noisy Air-Traffic Transcripts Using Surveillance Information

Air traffic control (ATC) relies on communication via speech between pilot and air-traffic controller (ATCO). The call-sign, as unique identifier for each flight, is used to address a specific pilot by the ATCO. Extracting the call-sign from the communication is a challenge because of the noisy ATC voice channel and the additional noise introduced by the receiver. A low signal-to-noise ratio (SNR) in the speech leads to high word error rate (WER) transcripts. We propose a new call-sign recognition and understanding (CRU) system that addresses this issue. The recognizer is trained to identify call-signs in noisy ATC transcripts and convert them into the standard International Civil Aviation Organization (ICAO) format. By incorporating surveillance information, we can multiply the call-sign accuracy (CSA) up to a factor of four. The introduced data augmentation adds additional performance on high WER transcripts and allows the adaptation of the model to unseen airspaces.
PU-Refiner: A Geometry Refiner with Adversarial Learning for Point Cloud Upsampling

We present PU-Refiner, a generative adversarial network for point cloud upsampling. The generator of our network includes a coarse feature expansion module to create coarse upsampled features, a geometry generation module to regress a coarse point cloud from the coarse upsampled features, and a progressive geometry refinement module to restore the dense point cloud in a coarse-to-fine fashion based on the coarse upsampled point cloud. The discriminator of our network helps the generator produce point clouds closer to the target distribution. It makes full use of multi-level features to improve its classification performance. Extensive experimental results show that PU-Refiner is superior to five state-of-the-art point cloud upsampling methods. Code: https://github.com/liuhaoyun/PU-Refiner.
Spatio-Temporal Graph Convolutional Networks for Continuous Sign Language Recognition

We address the challenging problem of continuous sign language recognition (CSLR) from RGB videos, proposing a novel deep-learning framework that employs spatio-temporal graph convolutional networks (ST-GCNs), which operate on multiple, appropriately fused feature streams, capturing the signer’s pose, shape, appearance, and motion information. In addition to introducing such networks to the continuous recognition problem, our model’s novelty lies on: (i) the feature streams considered and their blending into three ST-GCN modules; (ii) the combination of such modules with bi-directional long short-term memory networks, thus capturing both short-term embedded signing dynamics and long-range feature dependencies; and (iii) the fusion scheme, where the resulting modules operate in parallel, their posteriors aligned via a guiding connectionist temporal classification method, and fused for sign gloss prediction. Notably, concerning (i), in addition to traditional CSLR features, we investigate the utility of 3D human pose and shape parameterization via the "ExPose" approach, as well as 3D skeletal joint information that is regressed from detected 2D joints. We evaluate the proposed system on two well-known CSLR benchmarks, conducting extensive ablations on its modules. We achieve the new state-of-the-art on one of the two datasets, while reaching very competitive performance on the other.
Simultaneous Nonlocal Low-Rank And Deep Priors For Poisson Denoising

Poisson noise is a common electronic noise, which has widely occurred in various photo-limited imaging systems. However, due to signal-dependent and multiplicative characteristics for Poisson noise, Poisson denoising is still an open problem. In this paper, we propose a novel approach using simultaneous nonlocal low-rank and deep priors (SNLDP) for Poisson denoising. The proposed SNLD-P simultaneously employs nonlocal self-similarity and deep image priors under the hybrid plug and play framework, which comprises multiple pairs of complementary priors, namely, nonlocal and local, shallow and deep, and internal and external. To make the optimization tractable, an effective alternating direction method of multiplier (ADMM) algorithm under the alternative minimization framework is provided to solve the proposed SNLDP-based Poisson denoising problem. Experimental results demonstrate the superiority of the proposed SNLDP over many popular or state-of-the-art Poisson denoising algorithms in terms of quantitative and visual perception.
A novel bi-modal extended Huber loss function based refined mask RCNN approach for automatic multi instance detection and localization of breast cancer

Breast cancer is an extremely aggressive cancer in women. Its abnormalities can be observed in the form of masses, calcification and lumps. In order to reduce the mortality rate of women its detection is needed at an early stage. The present paper proposes a novel bi-modal extended Huber loss function based refined mask regional convolutional neural network for automatic multi-instance detection and localization of breast cancer. To refine and increase the efficacy of the proposed method three changes are casted. First, a pre-processing step is performed for mammogram and ultrasound breast images. Second, the features of the region proposal network are separately mapped for accurate region of interest. Third, to reduce overfitting and fast convergence, an extended Huber loss function is used at the place of SmoothL1(x) in boundary loss. To extend the functionality of Huber loss, the delta parameter is automated by the aid of median absolute deviation with grid search algorithm. It provides the best optimum value of delta instead of user-based value. The proposed method is compared with pre-existing methods in terms of accuracy, true positive rate, true negative rate, precision, F-score, balanced classification rate, Youden's index, Jaccard Index and dice coefficient on CBIS-DDSM and ultrasound database. The experimental result shows that the proposed method is a better suited approach for multi-instance detection, localization and classification of breast cancer. It can be used as a diagnostic medium that helps in clinical purposes and leads to a precise diagnosis of breast cancer abnormalities.
Deformable Convolution Dense Network for Compressed Video Quality Enhancement

Different from the traditional video quality enhancement, the goal of compressed video quality enhancement is to reduce the artifacts brought by the video compression. The existing multi-frame methods for compressed video quality enhancement heavily rely on optical flow, which is both inefficient and limited in performance. In this paper, a Multi-frame Residual Dense Network (MRDN) with deformable convolution is developed to improve the quality of the compressed video, by utilizing high-quality frame to compensate the low-quality frame. Specifically, the proposed network consists of the developed Motion Compensation (MC) module and Quality Enhancement (QE) module, aiming to compensate and enhance the quality of the input frame, respectively. Besides, a novel edge enhancement loss is conducted on the enhanced frame, in order to enhance edge structure during the training. Finally, the experimental results over a public benchmark show that our method outperforms the state-of-the-art methods for compressed video quality enhancement task.
Human Body Parts Measurement using Human Pose Estimation

In this Paper, the human body parts are measured using pose estimation method. Contour detection method is applied to detect the edges. This work aims to detect the body pose and track the sematic key points, which helps to measure the distances between various body parts. Media pipe model is used in this work, which is highly accurate for tracing sematic key-points in human body parts estimation. Edge detection method and CV2 are used to find the boundaries of the human body. In brief, this paper aims to provide a simple approach that will serve as a solid baseline for body parts measurement.
Deep Neural Network for Recognition of Enlarged Mathematical Corpus

Recent works influenced by the robust and outstanding performance of deep learning-based recognition models show the less attended research on an enlarged corpus of handwritten mathematical text. This article focuses on proposing a deep neural network-based model that has been processed and trained for a real-time dataset of mathematical expressions collected from school and university students of Punjab and Madhya Pradesh states of India. Mathematical expressions are part of education and knowledge-based science learning. Thus, predicting them on their handwritten source using a neural network is the novel approach of the article. A total of 236057 images have been obtained after segmentation, and the sample data has been collected from 1000 users. The proposed deep neural model exclusive of three dense layers (TrioNet) gives us an accuracy of 99.1% while working with 20 epochs.
Bi-level optimization for the energy conversion efficiency improvement in a photocatalytic-hydrogen-production system

Industrialization of the photocatalytic hydrogen production technology is plagued by the low energy conversion efficiency. To address this challenge, this study simultaneously optimizes the structure of the compound parabolic collector (CPC) and the operating conditions of the photoreactor to match the solar flow on the reactor surface and the energy required for chemical reaction at the catalyst interface by building a novel bi-level optimization model with partial differential equation (PDE) constraints. The Sobol method is used to identify the operating parameters that significantly influence the energy conversion efficiency. A new extreme learning machine (ELM) is developed as a surrogate model to approximate the relationship between the operating parameters of the reactor and hydrogen production, reducing computational load. A new nested algorithm is developed to solve the built bi-level optimization model. Numerical results indicate that the new method can find the optimal CPC structure and operating conditions for the photoreactor, achieving an increase in energy conversion efficiency as compared to the results from the systems with un-optimization, optimized CPC structure only and optimized reactor operating parameters only. Our study achieves the synergy of the CPC and photoreactor by combining the advanced optimization technology, machine learning and energy conversion process mechanisms.
Implementation of Education 4.0 as Sustainable Decisions for a Sustainable Development

excellent concept of sustainable development is an essential factor in addressing the world's rising unemployment rate. Many researchers believe that Industry 4.0 will increase unemployment besides replacing old jobs with automated machines such as robots, while others believe it will decrease underemployment by creating new employment opportunities. To prepare the millennial generation for the possible ramifications of Industry 4.0, Education 4.0 should be introduced. The purpose of this report is to examine the potential causes of youth unemployment in the age of Industry 4.0, to determine preventive measures or steps that can be taken, particularly by the educational system, to reduce the unemployment rate. According to root cause analysis (RCA), the problem might be caused by the lack of understanding of the critical elements of Education 4.0.
Broadcast signcryption scheme based on certificateless in wireless sensor network

In order to satisfy the confidentiality and integrity of the communication link data in the wireless sensor networks, and solve the key escrow problem of the identity-based signcryption scheme, a certificateless broadcast signcryption scheme is proposed. The scheme has dynamically adjusting authorization set without updating private key. In the encryption stage, since part of the operation is outsourced to a trusted third party to relieve the computational burden of the broadcaster, the scheme achieves lightweight broadcast encryption, which is suitable for resource-constrained devices in wireless sensor networks. The partial key is hidden in the secret value sequence to disguise the recipient’s identity and enhance the security of ciphertext. Lastly, we make the comparisons of the proposed work with the existing works in terms of functions and computational cost. In addition, performance evaluation shows that the scheme has higher efficiency in the signcryption and unsigncryption phase and is more suitable for practical broadcast service applications.
Segmentation and Contour Detection for handwritten mathematical expressions using OpenCV

Contour Detection owes its own significance in performing semantic segmentation and object classification. It is possible to recognize the edges of objects and readily locate them inside an image by employing contour detection. It is frequently the first step or achievable milestone in a varied range of exciting applications, such as extracting the foreground object from an image, classical-image segmentation methodology, identification or detection of the object, and many more. In this study, we have endeavored to perform contour detection using the OpenCV Adaptive Threshold method on the Aida handwriting math recognition dataset, containing ten batches of 10K images. Our experimentation successfully segmented the inputted mathematical expressions with an accuracy of 94.3% on the acquired dataset.
Adversarial Learning in Transformer Based Neural Network in Radio Signal Classification

Deep Learning has attracted significant interests in wireless communication design problems. However, recent studies discovered that the deep neural network is vulnerable to adversarial attacks in the sense that a carefully designed and imperceptible perturbation to the input of the neural network could mislead the prediction of the neural network. In this paper, motivated by attractive classification performance of the transformer based neural networks, we analyse the vulnerability and robustness of the transformer against adversarial attacks in modulation classification scenarios. Using real datasets, we demonstrate that the transformer can achieve higher accuracy as compared to a convolutional neural network in the presence of adversarial attacks.
Kernel-based gradient evolution optimization method

A new hybrid metaheuristic method is proposed which is inspired from the recently proposed gradient-evolution method. This study introduces for the first time the concept of reproducing kernel in order to correctly estimate the numerical gradient which is used as an updating rule. This proposed method is called Kernel-based Gradient Evolution (GE) which inherits good exploration and exploitation abilities as well as non localization through several operators taken from the original gradient-evolution algorithm. In the proposed KGE algorithm the gradient vector is computed using a kernel reproducing function. Thus the local gradient estimation is numerically computed considering a local Taylor series expansion. The IEEE CEC 2019 test suite is considered in order to evaluate the numerical performance of the proposed KGE algorithm. A numerical comparison between some competitive methods and the one proposed in this work is reported. The obtained numerical results suggested that in terms of convergence, KGE algorithm performs in most cases significantly better than the original GE algorithm and gets respectable results against other considered methods.
Monocular Vehicle 3D Bounding Box Estimation Using Homograhy and Geometry in Traffic Scene

Video surveillance applications such as vehicle speed measurement and traffic condition monitoring are prevailing nowadays. Monocular 3D object detection by traffic surveillance cameras is one of the key means to achieve these functions. The methods in literature depend on camera calibration and require 3D annotation to learn a model. In this paper, we propose a novel vehicle 3D bounding box estimation method making use of the 3D-2D geometry consistency and homography transformation. Our method conducts vehicle 3D bounding box estimation using uncalibrated traffic cameras without requiring any 3D annotation and large computational resources. With quantitative and qualitative experiments, we show a comparable result to the state-of-that art methods and a faster execution time.