Reviewerly/exHarmony huggingface dataset:
Single Image Deraining via Rain-Steaks Aware Deep Convolutional Neural Network

It is challenging to remove rain-steaks from a single rainy image because the rain steaks are spatially varying in the rainy image. This problem is studied in this paper by combining conventional image processing techniques and deep learning based techniques. An improved weighted guided image filter (iWGIF) is proposed to extract high frequency information from a rainy image. The high frequency information mainly includes rain steaks and noise, and it can guide the rain steaks aware deep convolutional neural network (RSADCNN) to pay more attention to rain steaks. The efficiency and explain-ability of RSADNN are improved. Experiments show that the proposed algorithm significantly outperforms state-of-the-art methods on both synthetic and real-world images in terms of both qualitative and quantitative measures. It is useful for autonomous navigation in raining conditions.
An Efficient Polynomial based Quantum Key Distribution Approach for Secure Communication

Cryptography is the process of converting information from readable to unreadable form in the world of computer security. However, there are still flaws at the cryptographic, channel, or implementation levels that need the creation or adoption of new and more robust security measures. The purpose of this study is to offer an efficient method for secure communication based on polynomial interpolation. Quantum cryptography ensures secure communication between authentic users by utilising the basic laws of quantum physics. One of the applications of quantum cryptography is quantum key distribution technology. The key used in the present protocol for secure communication is distinguished from the key created using the polynomial for the Quantum key distribution protocol. These two scenarios are performed on the QISKIT simulator, and their outcomes are assessed in a probabilistic manner. Furthermore, the presence and absence of eavesdropping in the channel are analysed. An environmental error, such as increased noise, may affect experiment performance.
Fast Hand Detection and Tracking for Human-Robot Interaction

The interaction channel between a cooperative robot and a human body is mainly through human hands, so human hand detection and tracking is an important step for the normal work of a cooperative robot. Here we have developed a fast and accurate hand detection and tracking technique. First, human skin is modeled with a Gaussian statistical distribution in YCbCr color space. Then the skin will be used as prior information for hand detection. The detected hand is tracked with particle filtering, and finally the contour of the hand is determined by the difference of Gaussian (DoG). This technique is very efficient and can overcome the influence of shape change and scale change of human hand as a non-rigid object in motion.
A Study on Anomaly Detection with Deep Learning Models for IoT Time Series Sensor Data

The high likelihood of noise and the lack of labels in Internet of Things (IoT) time-series sensor readings make the detection of abnormalities an important subject of study. When there is a high correlation between sensor data points, conventional data mining and machine learning algorithms are unable to detect abnormalities. Furthermore, the amount and speed with which IoT sensors report data is one of the reasons why traditional machine and statistical learning algorithms are unable to detect anomalies. In recent years, Artificial Neural Networks (ANN) have been widely used for developing Deep Learning models for learning and classifying unlabeled data for detecting anomalies with high accuracy. In this connection, this paper presents a study on the performance of two well-known ANN models, such as Generative Adversarial Network (GAN) and Variational Auto Encoder (VAE), and a classification model, One Class Support Vector Machine (OCSVM). To evaluate performance, simulations were conducted using a well-known network and sensor datasets. The outcomes show which technique outperformed the others in terms of detection accuracy and training duration.
Adaptive Latent Factor Analysis via Generalized Momentum-Incorporated Particle Swarm Optimization

Stochastic gradient descent (SGD) algorithm is an effective learning strategy to build a latent factor analysis (LFA) model on a high-dimensional and incomplete (HDI) matrix. A particle swarm optimization (PSO) algorithm is commonly adopted to make an SGD-based LFA model's hyper-parameters, i.e., learning rate and regularization coefficient, self-adaptation. However, a standard PSO algorithm may suffer from accuracy loss caused by premature convergence. To address this issue, this paper incorporates more historical information into each particle's evolutionary process for avoiding premature convergence following the principle of a generalized-momentum (GM) method, thereby innovatively achieving a novel GM-incorporated PSO (GM-PSO). With it, a GM-PSO-based LFA (GMPL) model is further achieved to implement efficient self-adaptation of hyper-parameters. The experimental results on three HDI matrices demonstrate that the GMPL model achieves a higher prediction accuracy for missing data estimation in industrial applications.
A Dual Learning Strategy-based Particle Swarm Optimization with Adaptive Velocity Control

Particle swarm optimization has been paid more attention in recent decades, since its simplicity and effectiveness. Numerous variants of particle swarm optimization have been proposed for solving various optimization problems in recent years. Learning strategy between particles significantly affects their search behaviour and performance. Although various novel learning strategies have been introduced into particle swarm optimization, most of them are only use one type of mechanisms. In this paper, we propose a dual learning strategy-based particle swarm optimization with adaptive velocity control. The proposed algorithm has good exploration and exploitation which is benefited from comprehensive learning and genetic learning, respectively. Furthermore, these two abilities are well balanced according to adaptive parameter and velocity control. The proposed algorithm is tested on 30 frequently used benchmark functions, and the experimental results show that it outperforms other learning strategy-based particle swarm optimization algorithms in terms of effectiveness and robustness.
Context-aware assistance guidance via augmented reality for industrial human-robot collaboration

Industrial human-robot collaboration (HRC) endows industrial robots with the ability to cooperate with workers and complete common production goals. In the HRC scenario, the dynamic and unstructured environment makes it difficult for both humans and robots to collaborate quickly, where an intuitive communication channel for assistance guidance is desired. This paper proposes an augmented reality (AR)-based assistance guidance approach with an improved context-aware recommendation algorithm. The defined HRC-specified context information is extracted and analyzed from the real-time perceived data. The context model is then updated and triggers the guidance event to derive multiple options of assistance guidance views. Finally, a novel context-aware recommendation algorithm based on user-collaborative filtering is proposed to obtain the optimal AR view for assistance guidance. The proposed method is validated in a developed HRC assembly case. The result shows that the proposed method performs better than the other three recommendation algorithms in terms of accuracy, recall, and MAP, which promises to improve the efficiency of human-robot collaboration and reduce the learning cost for HRC tasks.
Generative Image Inpainting for Fine Details

Since the rapid development of deep learning, image inpainting techniques have also improved significantly. Although these techniques have been able to reconstruct semantically coherent and visually plausible masked regions compared to traditional techniques, the results of these works are commonly blurry due to lack fine details. This paper proposes a novel model including an image completion network and an edge matching module. The image completion network is a Generative Adversarial Network framework added skip-connection for contextual feature fusion, and the edge matching network facilitates the image inpainting network by constraining the edge of results. We evaluate our model on the publicly available datasets CelebA and Places2. Results show that our method outperforms current representative technique quantitatively and qualitatively.
Model of Gradient Boosting Random Forest Prediction

Random forests (RF) is an ensemble classification approach, which is easy to use and is helpful to avoid over-fitting. However, in the complex data environment, its prediction accuracy could be deteriorated. Gradient boosting decision tree (GBDT) is another widely used in classification problems because of its high prediction accuracy and interpretability. In order to improve the performance of random forest in solving classification problems, this paper proposes a gradient boosting random forest (GBRF) algorithm. GBRF algorithm employs the idea of gradient to optimize decision tree at the bottom of random forest into gradient boosting decision tree, which improves the prediction accuracy of the bottom tree, and thus improves the prediction performance of random forest. To verify the effectiveness of GBRF algorithm, data sets in UCI and KEEL are used for group testing. The results show that the classification accuracy of GBRF algorithm has a higher prediction accuracy improvement compared to random forest and the performance improvement is more than 5 percent, which indicates that GBRF algorithm performs better than the original random forest.
Performance Analysis of Conditional GANs based Image-to-Image Translation Models for Low-Light Image Enhancement

With the evolution of generative adversarial networks, popularly known as GANs for image-to-image translations, conditional GANs (cGANs) are explored and employed for various digital image preprocessing (enhancement and de-noising) tasks. The series of tasks includes image processing such as image enhancement, de-hazing, de-noising, resolution enhancement, and many more. In image enhancement, the area of increasing light (brightness) in low-light images (or poorly-illuminated images) is investigated in this work. For low-light image enhancement, the performance of pix2pix and pix2pixHD models has been demonstrated and analyzed. An analysis of low-light image enhancement using pix2pix model with other loss functions is also presented. Furthermore, pix2pix performance with instance normalization layers for low-light image enhancement is studied, and improved full-reference Image Quality Assessment (FIQA) metrics values along with entropy (a no-reference IQA (NIQA) metric) are reported. The quantitative and qualitative results are also compared with selected cutting-edge deep learning frameworks for low-light image enhancement. In this research, it is found that pix2pix model enhancement metrics are better than RetinexNet model. And the pix2pixHD results are comparable to the latest low-light image enhancement deep learning frameworks such as MIRNet and LLFlow. Furthermore, pix2pix models are lighter in size than MIRNet. The inference times achieved using pix2pix are the minimum on both the CPU and the GPU.
Comparative Analysis of Machine Learning Models for Fitness Level Prediction with Imbalanced Dataset

An individual's fitness level is usually synonymous to his or her health status. While some people may quite often feel healthy with no health issues, others may suffer from a myriad of health-related problems. In general, people will seek the advice of health professionals as the last resort, when their health issues surface. In this research, we aim to use fitness-related data to predict an individual's fitness level using the machine learning approach. However, with an imbalanced dataset, we first use Synthetic Minority Oversampling Technique (SMOTE) to balance the dataset. Six machine learning techniques; Naïve Bayes (NB), Support Vector Machine (SVM), Logistic Regression (LR), K-Nearest Neighbour (KNN), Decision Tree (DT) and Random Forest (RF) were then used to build models for predicting fitness levels of “Fit”, “Average” and “Unfit”. A comparative study using ten- fold cross validation was then conducted to determine the best model, using both the imbalanced and balanced datasets. Experimental results showed that Random Forest performed the best in predicting the fitness level of an individual with an accuracy and F1-score of over 90% when using the balanced dataset. This showed the potential of using this model for an application to predict one's fitness level.
Acquisition, Processing and Applications of EOG Signals

We are living in the era of a blend of information technology, artificial intelligence, and virtual reality. This rapid transition and development have affected every sphere of life and medical sciences and clinical approaches are not exceptions. This paper discusses the methods of acquisition, processing and applications of Electro Oculography (EOG) signals, a bio signal generated by different eye movements. The use of EOG signals can facilitate a very good Human Machine / Computer interface.
Temperature Control of Hot Wind Tunnel by Using Cascade Integral Separated PI and Proportion Control Law

Temperature control is an important index to evaluate the performance of hot wind tunnel. Therefore, this paper designs a cascade integral separated proportion integral and proportion(PI-P) control law to control the temperature of the hot wind tunnel. The mathematical models of the fuel oil flow rate and combustor temperature are established. The reference flow rate of the flow rate loop is obtained by querying the temperature-flow (T-q) table, and then reference flow rate and the compensation flow rate obtained by the temperature control algorithm are superposed to reach the target temperature. P control is adopted to control the fuel oil flow rate in the inner loop by controlling the position of the digital valve. The integral separated PI control is used to control the temperature in the outer loop. The step response at 1200℃, 1400℃ and 1600℃ is simulated and tested. The results show that the cascade integral separated PI-P control method proposed in this paper has good rapidity and stability.
An Effective End-to-End Image Matching Network with Attentional Graph Neural Networks

Established feature-based image matching algorithms usually use two networks to obtain the features and matching correspondences of images separately. But then the former network cannot know the results of the operations of the latter network, and backpropagation cannot be performed to optimize the parameters. For the image feature point matching problem, an end-to-end deep learning algorithm is designed. The neural network takes a pair of images as input and obtains feature keypoints, descriptors, and matching correspondences directly after a series of network layers. The advantages of the end-to-end algorithm are verified by comparing the end-to-end training strategies with independence training strategies.
An Improved Speed Estimation Using Deep Homography Transformation Regression Network on Monocular Videos

Vehicle speed estimation is one of the most critical issues in intelligent transportation system (ITS) research, while defining distance and identifying direction have become an inseparable part of vehicle speed estimation. Despite the success of traditional and deep learning approaches in estimating vehicle speed, the high cost of deploying hardware devices to get all related sensor data, such as infrared/ultrasonic devices, Global Positioning Systems (GPS), Light Detection and Ranging (LiDAR systems), and magnetic devices, has become the key barrier to improvement in previous studies. In this paper, our proposed model consists of two main components: 1) a vehicle detection and tracking component – this module is designed for creating reliable detection and tracking every specific object without doing calibration; 2) homography transformation regression network – this module has a function to solve occlusion issues and estimate vehicle speed accurately and efficiently. Experimental results on two datasets show that the proposed method outperforms the state-of-the-art methods by reducing the mean square error (MSE) metric from 14.02 to 6.56 based on deep learning approaches. We have announced our test code and model on GitHub with <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/ervinyo/Speed-Estimation-Using-Homography-Transformation-and-Regression-Network</uri> .
Robust Speaker Verification Using Deep Weight Space Ensemble

Domain shift is one of the most challenging problems in speaker verification. Although numerous methods have been proposed to address domain shift, most approaches optimize the performance of one domain at the sacrifice of the other. As a result, to obtain the best performance, each domain requires a dedicated model. However, deploying multiple models is resource-demanding and impractical, particularly when the deployment domains are not known in advance. Recent studies in deep neural networks (DNNs) suggest that near the low error surface of the DNN's weight space, there exists a linear path connecting a base model and a fine-tuned model. This finding inspires us to combine the strength of the fine-tuned models and the base models to solve challenging SV problems. Specifically, we aim to develop models that can handle 1) mixed text-dependent (TD) and text-independent (TI) speaker verification where the speech content can be either unconstrained or constrained, 2) cross-channel speaker verification where the recording can be 16 kHz high-fidelity microphone speech or 8 kHz telephone speech, and 3) bi-lingual speaker verification where the enrollment and test speech can be one of the two languages. With weight space ensemble, we show that we can substantially improve the tasks mentioned above, with a 39.6% improvement in mixing TD and TI SV, a 17.4% improvement in bi-lingual SV, and an 18.4% improvement in cross-channel SV. Moreover, we show that the weight space ensemble can also enhance the performance in the target domain, thanks to the regularization effect of the interpolation.
ML-Augmented Automation for Recovering Links Between Pull-Requests and Issues on GitHub

GitHub provides a distributed and collaborative platform to develop and maintain open-source projects. This social coding platform achieves this collaborative development, with or without coordination, using pull requests and issues artefacts. When the number of daily submitted issues rapidly grows up, especially in popular repositories, managing issues becomes more complicated. To help the repository’s developers in issues processing, there are external contributors who fix issues by submitting pull-requests. On GitHub, a pull-request is frequently linked with a submitted issue to show that a solution is in progress. Unfortunately, contributors might be lazy or forget to link the Pull-Requests with their corresponding Issues. Only a very small share of these links are established, whereas a large portion of links is missed in the development history. In spite of that, even for senior developers, manually recovering the links between Pull-Request and Issues from evolutionary development history is a time-consuming, challenging, and error-prone task. In this article, we propose to build ML models to recover links between pull-requests and their issues using two Machine Learning algorithms (KMeans and BIRCH) based on lexical and semantic weighting measurements. These models are evaluated using PI-Link ground-truth dataset. The obtained results show that pull-request and issue links can be recovered with an accuracy of 91.5% using BIRCH clustering algorithm.
Achieving Online and Scalable Information Integrity by Harnessing Social Spam Correlations

Malicious web links, social rumors, fraudulent advertisements, faked comments, and biased propaganda are overwhelmingly influencing online social networks. Enabling information integrity is a hot topic in both academia and industry. Traditional social spam detection techniques rely on centralized processing, focusing only on one specific set of data sources, thereby ignoring the social spam correlations between distributed data sources. In this paper, we propose an online and scalable misinformation detection system, named Spiral, to uncover social spam by leveraging the correlations between different social data sources in geo-distributed sites. The key insight in our approach is to amplify the effectiveness of state-of-the-art techniques to detect inappropriate posts by enabling the efficient large-scale propagation of detection information across domains. The novelty of our design lies in three key components: (1) a decentralized distributed hash-table-based tree overlay deployment for harvesting and uncovering deceptive information spreading in multiple online social networks communities; (2) a progressive aggregation tree for collecting the properties of these posts and creating new classifiers to actively filter out the propagation of inappropriate posts; and (3) a group communication structure that allows multiple groups to exchange the correlations among distributed social data sources. We designed and implemented a prototype of the Spiral system. Our large-scale experiments, using real-world social data, demonstrate Spiral’s scalability, effective load-balancing, and efficiency in online spam detection for social networks.
Eco-CSAS: A Safe and Eco-Friendly Speed Advisory System for Autonomous Vehicle Platoon Using Consortium Blockchain

Future worldwide 6G research will drive the evolution of emerging intelligent control technologies, such as intelligent speed advisory systems (ISA), to a more advanced generation. As a special type of ISA, consensus-based speed advisory systems (CSAS) can be widely used to recommend a consensus speed for a vehicle platoon, enabling minimizing energy consumption or emissions over a planned route. Recently, speed recommendation services that protect data privacy (i.e., how to obtain an optimal speed in a privacy-preserving way) have drawn tremendous attention. However, current approaches could still encounter service trust issues with central servers and the malicious behavior of vehicles. Furthermore, existing research lacks considering road safety constraints (i.e., safe distance between adjacent vehicles and road speed limits) that are essential for the practical deployment of CSAS. To address the above issues, this paper proposes Eco-CSAS, a safe and eco-friendly consensus speed advisory system using blockchain. We formulate an optimization problem subject to the minimum following distance and maximum road speed limit to minimize the energy consumption of the automatic vehicle platoon. In addition, we introduce a consortium blockchain and cryptographic primitives to ensure service trust and data privacy. We implement the system on the Hyperledger platform, and experimental results show that the system can achieve speed recommendations in a trustworthy and privacy-preserving manner while ensuring a secure platoon.
A Construction of Maximum Distance Profile Convolutional Codes With Small Alphabet Sizes

Convolutional codes are essential in a wide range of practical applications due to their efficient non-algebraic decoding algorithms. In this paper, we first propose a new family of matrices over finite fields by combining Vandermonde and Moore matrices. Using favourable properties of the matrices in this new family enables us to construct a new family of convolutional codes with memory 1 and maximum distance profile. It is notable that the alphabet sizes of this new family of convolutional codes with maximum distance profile can be kept significantly smaller than those in the literature. Keeping the code rate to a constant, the alphabet size is roughly the square root of the previously best-known value.