Reviewerly/exHarmony huggingface dataset:
Vision-based Detection, Tracking, and Classification of Vehicles

Motion capturing involves the identification of objects in motion and plotting their motion by analyzing them. When considered in regard to a video sequence, motion capturing can be defined as a procedure of unmasking objects in motion in sequences of frames using exhaustive and proficient digital image processing techniques. Methodology: To ingress need contrasting time for trial and their execution shows difference in terms of pace and memory requisites. Results: The result obtained shows the performance of the algorithms and models in the given conditions. It also reflects the best suited environment for the techniques. The work was accomplished by testing the algorithms for numerous sequences of input video. The identification rate analysis gives the identification rate of foreground pixels for various colors divergent from the background model. Conclusions: The objective of computer vision is to imitate human vision utilizing advanced digital images through three principle handling parts that are executed consistently, i.e., acquisition of pictures, picture processing, and picture investigation and comprehension. It has gained a lot of attention from researchers in an enormous field. The basic purpose of this study was to prospecting tracking and computer-aided object detection techniques. For this purpose, a number of methods were observed, studied critically, and used. Originality: In the study, an improved frame differencing method for tracking an object is briefly reviewed, and an improved version of the algorithm was also implemented using MATLAB. The proposed algorithm was tested over distinct video sequences, and it was observed that the objects in motion were identified with minimal error rate when compared to a traditional frame differencing method. Limitations: One of the major challenges in the process of tracking objects in motion is to design algorithms or techniques for tracking the objects present in disrupted or random videos, like videos attained from broadcast news networks or home videos. These videos contain noise, and some of them may be unstructured, compressed, and denominationally having edited pieces obtained in different ways by moving cameras.
Synthetic data with neural machine translation for automatic correction in arabic grammar

The automatic correction of grammar and spelling errors is important for students, second language learners, and some Natural Language Processing (NLP) tasks such as part of speech and text summarization. Recently, Neural Machine Translation (NMT) has been an out-performing and well-established model in the task of Grammar Error Correction (GEC). Arabic GEC is still growing because of some challenges, such as scarcity of training sets and the complexity of Arabic language. To overcome these issues, we introduced an unsupervised method to generate large-scale synthetic training data based on confusion function to increase the amount of training set. Furthermore, we introduced a supervised NMT model for AGEC called SCUT AGEC. SCUT AGEC is a convolutional sequence-to-sequence model consisting of nine encoder-decoder layers with attention mechanism. We applied fine-tuning to improve the performance and get more efficient results. Convolutional Neural Networks (CNN) gives our model ability to joint feature extraction and classification in one task and we proved that it is an efficient way to capture features of the local context. Moreover, it is easy to obtain long-term dependencies because of convolutional layers staking. Our proposed model becomes the first supervised AGEC system based on the convolutional sequence-to-sequence learning to outperforms the current state-of-the-art neural AGEC models.
FUCOM-MOORA and FUCOM-MOOSRA: new MCDM-based knowledge-driven procedures for mineral potential mapping in greenfields

Abstract In this study, we present the application of two novel hybrid multiple-criteria decision-making (MCDM) techniques in the mineral potential mapping (MPM), namely FUCOM-MOORA and FUCOM-MOOSRA, as robust computational frameworks for MPM. These were applied to a set of exploration targeting criteria of skarn. The multi-objective optimization method on the basis of ratio analysis (MOORA) and the multi-objective optimization on the basis of simple ratio analysis (MOOSRA) approaches are used to prioritize and rank individual cells. What makes MOORA and MOOSRA more reliable compared to many other methods is the fact that the optimizations procedure is applied to calculate the prospectivity score of individual unit cells. This reduces the uncertainty stemming from erroneous mathematical calculations. The full consistency method (FUCOM), on the other hand, is useful for assigning weights to the spatial proxies. The FUCOM method, as a pairwise comparison method, reduces a large number of pairwise comparisons of similar and popular approaches such as analytic hierarchy process (AHP) with $$n\left( {n - 1} \right)/2$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mi>n</mml:mi> <mml:mfenced> <mml:mrow> <mml:mi>n</mml:mi> <mml:mo>-</mml:mo> <mml:mn>1</mml:mn> </mml:mrow> </mml:mfenced> <mml:mo>/</mml:mo> <mml:mn>2</mml:mn> </mml:mrow> </mml:math> and the best–worst method (BWM) with $$2n - 3$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mn>2</mml:mn> <mml:mi>n</mml:mi> <mml:mo>-</mml:mo> <mml:mn>3</mml:mn> </mml:mrow> </mml:math> number of pairwise comparisons with $$n - 1$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mi>n</mml:mi> <mml:mo>-</mml:mo> <mml:mn>1</mml:mn> </mml:mrow> </mml:math> which leads to a less time-consuming and more consistent performance compared with AHP and BWM. These were applied to a set of exploration targeting criteria of skarn iron deposits from Central Iran. Two potential maps were retrieved from the procedures applied, the comparison of which using correct classification rates and field checks revealed the superiority of FUCOM-MOOSRA over the FUCOM-MOORA.
Self-supervised monocular depth estimation from oblique UAV videos

Unmanned Aerial Vehicles (UAVs) have become an essential photogrammetric measurement as they are affordable, easily accessible and versatile. Aerial images captured from UAVs have applications in small and large scale texture mapping, 3D modelling, object detection tasks, Digital Terrain Model (DTM) and Digital Surface Model (DSM) generation etc. Photogrammetric techniques are routinely used for 3D reconstruction from UAV images where multiple images of the same scene are acquired. Developments in computer vision and deep learning techniques have made Single Image Depth Estimation (SIDE) a field of intense research. Using SIDE techniques on UAV images can overcome the need for multiple images for 3D reconstruction. This paper aims to estimate depth from a single UAV aerial image using deep learning. We follow a self-supervised learning approach, Self-Supervised Monocular Depth Estimation (SMDE), which does not need ground truth depth or any extra information other than images for learning to estimate depth. Monocular video frames are used for training the deep learning model which learns depth and pose information jointly through two different networks, one each for depth and pose. The predicted depth and pose are used to reconstruct one image from the viewpoint of another image utilising the temporal information from videos. We propose a novel architecture with two 2D Convolutional Neural Network (CNN) encoders and a 3D CNN decoder for extracting information from consecutive temporal frames. A contrastive loss term is introduced for improving the quality of image generation. Our experiments are carried out on the public UAVid video dataset. The experimental results demonstrate that our model outperforms the state-of-the-art methods in estimating the depths.
privGAN: Protecting GANs from membership inference attacks at low cost to utility

Abstract Generative Adversarial Networks (GANs) have made releasing of synthetic images a viable approach to share data without releasing the original dataset. It has been shown that such synthetic data can be used for a variety of downstream tasks such as training classifiers that would otherwise require the original dataset to be shared. However, recent work has shown that the GAN models and their synthetically generated data can be used to infer the training set membership by an adversary who has access to the entire dataset and some auxiliary information. Current approaches to mitigate this problem (such as DPGAN [1]) lead to dramatically poorer generated sample quality than the original non–private GANs. Here we develop a new GAN architecture (privGAN), where the generator is trained not only to cheat the discriminator but also to defend membership inference attacks. The new mechanism is shown to empirically provide protection against this mode of attack while leading to negligible loss in downstream performances. In addition, our algorithm has been shown to explicitly prevent memorization of the training set, which explains why our protection is so effective. The main contributions of this paper are: i) we propose a novel GAN architecture that can generate synthetic data in a privacy preserving manner with minimal hyperparameter tuning and architecture selection, ii) we provide a theoretical understanding of the optimal solution of the privGAN loss function, iii) we empirically demonstrate the effectiveness of our model against several white and black–box attacks on several benchmark datasets, iv) we empirically demonstrate on three common benchmark datasets that synthetic images generated by privGAN lead to negligible loss in downstream performance when compared against non– private GANs. While we have focused on benchmarking privGAN exclusively on image datasets, the architecture of privGAN is not exclusive to image datasets and can be easily extended to other types of datasets. Repository link: https://github.com/microsoft/privGAN .
Privacy reinforcement learning for faults detection in the smart grid

Recent anticipated advancements in ad hoc Wireless Mesh Networks (WMN) have made them strong natural candidates for Smart Grid’s Neighborhood Area Network (NAN) and the ongoing work on Advanced Metering Infrastructure (AMI). Fault detection in these types of energy systems has recently shown lots of interest in the data science community, where anomalous behavior from energy platforms is identified. This paper develops a new framework based on privacy reinforcement learning to accurately identify anomalous patterns in a distributed and heterogeneous energy environment. The local outlier factor is first performed to derive the local simple anomalous patterns in each site of the distributed energy platform. A reinforcement privacy learning is then established using blockchain technology to merge the local anomalous patterns into global complex anomalous patterns. Besides, different optimization strategies are suggested to improve the whole outlier detection process. To demonstrate the applicability of the proposed framework, intensive experiments have been carried out on well-known CASAS (Center of Advanced Studies in Adaptive Systems) platform. Our results show that our proposed framework outperforms the baseline fault detection solutions.
Cloud task scheduling using enhanced sunflower optimization algorithm

The objective of cloud task scheduling is to partition tasks on shared resources to minimize energy consumption and makespan. Recently, several meta-heuristics for task scheduling were proposed and achieved encouraging results. However, their performance is far from the ideal state and needs more improvement. This paper introduces an enhanced sunflower optimization (ESFO) algorithm for improving the performance of existing task scheduling. It finds optimal scheduling in a polynomial time. The experiments show that ESFO outperformed its counterparts. The amount of improvement in comparison with the best counterpart is 0.73% and 2.24% respectively in terms of makespan and energy consumption.
Balanced single-shot object detection using cross-context attention-guided network

In real-world application scenarios, object detection usually encounters two technical challenges, i.e., high accuracy and high speed. Although the latest detection frameworks based on anchor-free detection have achieved outstanding performance, they cannot be widely used in real-world scenarios due to their model complexity and slow speed. In this paper, inspired by cross-context attention mechanism of human visual systems, we propose a light but effective single-shot detection framework using Cross-context Attention-guided Network (CCAGNet) to balance the accuracy and speed. CCAGNet uses attention-guided mechanism to highlight the interaction of object-synergy regions, and suppresses non-object-synergy regions by combining Cross-context Attention Mechanism (CCAM), Receptive Field Attention Mechanism (RFAM), and Semantic Fusion Attention Mechanism (SFAM). The main contribution of our work includes establishing a novel attention mechanism that takes the context information of channel, spatial, cross- and adjacent-regions into consideration simultaneously. Extensive experiments demonstrate the feasibility and effectiveness of our method on the public benchmark datasets. To the best of our knowledge, CCAGNet obtains the state-of-the-art performance on both PascalVOC and MSCOCO with the excellent trade-off between accuracy and speed among single-shot detectors. Especially, the Average Precision (AP) metric is significantly improved by 17.0% on small object detection on MSCOCO.
Evolutionary Dual-Ensemble Class Imbalance Learning for Human Activity Recognition

Human activity recognition is an imbalance classification problem in essence since various human actions may occur at different frequencies. Traditional ensemble class imbalance learning methods integrate resampling technique with multi-classifier models, which obtain better generalization than single ones. However, the number of base classifiers is often determined in advance, resulting in the redundant structure of a multi-classifier and larger computational cost. Moreover, various combinations of base classifiers may have similar recognition accuracy, forming a multimodel optimization problem. To address the issue, a dual-ensemble class imbalance learning method is presented, in which two ensemble learning models are nested each other. For each sub-dataset, three heterogeneous sub-classifiers compose of internal ensemble learning model, and the one with the highest recognition accuracy is selected as base classifier. In external ensemble learning model, an optimal combination that contains the smallest number of base classifiers and identifies human actions most exactly is found by multimodal evolutionary algorithms. Based on seven imbalanced activity datasets derived from localization data for person activity, statistical experimental results show that the proposed evolutionary dual-ensemble class imbalance learning method provides the simplest ensemble structure with the best accuracy for imbalanced data that outperforms other five widely-used ensemble classification methods on all metrics.
Triple <i>N</i>-Step Phase Shift Algorithm for Phase Error Compensation in Fringe Projection Profilometry

For fringe projection profilometry (FPP), the nonlinear response of the camera and projector in a 3D measurement system causes the captured fringe to be non-sinusoidal, which results in periodic phase errors. This paper reports a theoretical analysis of the phase error caused by the non-sinusoidal fringe, and proposes a triple N-step phase shift (PS) algorithm. Experimental results demonstrate that the proposed algorithm outperforms the existing double N-step phase shift algorithm in terms of reducing the phase error caused by the non-sinusoidal captured fringe.
FCAF3D: Fully Convolutional Anchor-Free 3D Object Detection

Recently, promising applications in robotics and augmented reality have attracted considerable attention to 3D object detection from point clouds. In this paper, we present FCAF3D—a first-in-class fully convolutional anchor-free indoor 3D object detection method. It is a simple yet effective method that uses a voxel representation of a point cloud and processes voxels with sparse convolutions. FCAF3D can handle large-scale scenes with minimal runtime through a single fully convolutional feed-forward pass. Existing 3D object detection methods make prior assumptions on the geometry of objects, and we argue that it limits their generalization ability. To eliminate prior assumptions, we propose a novel parametrization of oriented bounding boxes that allows obtaining better results in a purely data-driven way. The proposed method achieves state-of-the-art 3D object detection results in terms of mAP@0.5 on ScanNet V2 (+4.5), SUN RGB-D (+3.5), and S3DIS (+20.5) datasets. The code and models are available at https://github.com/samsunglabs/fcaf3d .
Interpretable Machine Learning

The emergence of machine learning as a society-changing technology in the past decade has triggered concerns about people's inability to understand the reasoning of increasingly complex models. The field of IML (interpretable machine learning) grew out of these concerns, with the goal of empowering various stakeholders to tackle use cases, such as building trust in models, performing model debugging, and generally informing real human decision-making.
Robust Medical Image Classification From Noisy Labeled Data With Global and Local Representation Guided Co-Training

Deep neural networks have achieved remarkable success in a wide variety of natural image and medical image computing tasks. However, these achievements indispensably rely on accurately annotated training data. If encountering some noisy-labeled images, the network training procedure would suffer from difficulties, leading to a sub-optimal classifier. This problem is even more severe in the medical image analysis field, as the annotation quality of medical images heavily relies on the expertise and experience of annotators. In this paper, we propose a novel collaborative training paradigm with global and local representation learning for robust medical image classification from noisy-labeled data to combat the lack of high quality annotated medical data. Specifically, we employ the self-ensemble model with a noisy label filter to efficiently select the clean and noisy samples. Then, the clean samples are trained by a collaborative training strategy to eliminate the disturbance from imperfect labeled samples. Notably, we further design a novel global and local representation learning scheme to implicitly regularize the networks to utilize noisy samples in a self-supervised manner. We evaluated our proposed robust learning strategy on four public medical image classification datasets with three types of label noise, i.e., random noise, computer-generated label noise, and inter-observer variability noise. Our method outperforms other learning from noisy label methods and we also conducted extensive experiments to analyze each component of our method.
A neural network learning algorithm for highly imbalanced data classification

The imbalanced data problem exists in many real-world datasets. Neural networks are one popular method for classifying imbalanced data. However, data imbalance often negatively affects neural networks. This problem is exacerbated when data are highly imbalanced. Existing neural network approaches for handling this problem heavily rely on rebalancing or reweighting known data. Essentially, these strategies focus on recovering the characteristics of balanced data. However, due to the serious lack of positive samples, the problem of insufficient empirical representation has not been thoroughly considered. Therefore, to solve the problem of highly imbalanced data, we explore the characteristics of the gradient norm in gradient descent optimization. We find that the key indicator of balanced data is that the gradient norms of positive and negative classes are approximately equal. Specifically, neural networks can classify known data that is highly imbalanced by considering the unit gradient direction of positive and negative classes. Furthermore, a local boundary expansion strategy is considered to alleviate the insufficient empirical representation problem of the positive class. Here, we propose a controllable gradient rotation strategy to realize local boundary expansion for positive samples. We validate the proposed approach on 34 highly imbalanced datasets and two synthetic datasets , and the proposed method exhibits impressive performance.
Multi-task deep learning for medical image computing and analysis: A review

The renaissance of deep learning has provided promising solutions to various tasks. While conventional deep learning models are constructed for a single specific task, multi-task deep learning (MTDL) that is capable to simultaneously accomplish at least two tasks has attracted research attention. MTDL is a joint learning paradigm that harnesses the inherent correlation of multiple related tasks to achieve reciprocal benefits in improving performance, enhancing generalizability, and reducing the overall computational cost. This review focuses on the advanced applications of MTDL for medical image computing and analysis. We first summarize four popular MTDL network architectures (i.e., cascaded, parallel, interacted, and hybrid). Then, we review the representative MTDL-based networks for eight application areas, including the brain, eye, chest, cardiac, abdomen, musculoskeletal, pathology, and other human body regions. While MTDL-based medical image processing has been flourishing and demonstrating outstanding performance in many tasks, in the meanwhile, there are performance gaps in some tasks, and accordingly we perceive the open challenges and the perspective trends. For instance, in the 2018 Ischemic Stroke Lesion Segmentation challenge, the reported top dice score of 0.51 and top recall of 0.55 achieved by the cascaded MTDL model indicate further research efforts in high demand to escalate the performance of current models.
The Unreasonable Effectiveness of Texture Transfer for Single Image Super-Resolution

While implicit generative models such as GANs have shown impressive results in high quality image reconstruction and manipulation using a combination of various losses, we consider a simpler approach leading to surprisingly strong results. We show that texture loss [1] alone allows the generation of perceptually high quality images. We provide a better understanding of texture constraining mechanism and develop a novel semantically guided texture constraining method for further improvement. Using a recently developed perceptual metric employing “deep features” and termed LPIPS [2], the method obtains state-of-the-art results. Moreover, we show that a texture representation of those deep features better capture the perceptual quality of an image than the original deep features. Using texture information, off-the-shelf deep classification networks (without training) perform as well as the best performing (tuned and calibrated) LPIPS metrics.