Reviewerly/exHarmony huggingface dataset:
An Enhancing Grasshopper Optimization for Efficient Feature Selection

Grasshopper optimization algorithm (GOA) is a new swarm intelligence algorithm that simulated a model of locust swarm behavior for optimization problems. Still, it has drawbacks, e.g., falling into a local optimum and slow convergence. This paper suggests a method enhancing GOA optimization (namely EGOA) based on an adaptive weight coefficient and nonlinear parameters to balance the global exploration and local development capabilities, to promote the convergence speed, and to avoid trapping of falling into a local optimum the algorithm. In the simulation experiment, four benchmark test functions and the feature selection problem are used to prove that the improved strategy used in the proposed method can effectively enhance the precision and convergence speed of the GOA. The experimental results on seven UCI datasets show that the proposed method can effectively provide the best selection features for increasing classification accuracy.
Music panel: An application for creating and editing music using OpenCV and JFugue

Music is the product of human creativity and the mastery of musical instruments. There are a large number of musical instruments, whether electric, acoustic, wind or percussion. Some areas of research explore the creation of music through the human-computer interaction. This paper proposes a way to generate sounds with the help of a wall-installed panel with notes drawn on it. The notes are recognized with a webcam and computer vision algorithms, and then played back to the user in real time. The application was created with the aim of giving people who want to express their creativity through music, the chance to do so without the need for musical instruments, in a simple and novel way.
Real-time vehicle detection under complex road conditions

Vehicle detection is an important component in unmanned driving systems. This paper presents a real-time vehicle detection method under complex road conditions to solve the problem of real-time detection of road vehicles in automatic driving. Firstly using yolo network to build a deeper depth neural network, which is used to identify and score the vehicles in the image. Then, using the dynamic threshold method to remove some false candidate boxes, and using the Gauss attenuation function to filter the overlapping candidate boxes. The loss function normalized by the change rate is used to train the neural network, and the batch normalization layer is used to correct the input data of the network to avoid data deviation during the training process. Finally, a full convolution layer is added at the end of the network layer to transform the two-dimensional data into one-dimensional data and output the final recognition results. It is verified that this method improves the efficiency and accuracy of real-time vehicle detection, it can effectively detect vehicles on roads with complex backgrounds, and satisfy the real-time requirements of road vehicle detection.
Remote Sensing Image Color Correction Method Based on Automatic Piecewise Polynomial Method

In the process of remote sensing image restoration, color correction is very important. The polynomial algorithm color correction method based on standard color card is the most commonly used method. However, the traditional polynomial fitting method needs to determine the most appropriate combination of polynomials, and only one polynomial function fitting method is used. So it is difficult to guarantee high accuracy and good generalization performance at the same time. In order to solve the above problems effectively, this paper proposes an automatic piecewise polynomial fitting method. This study established the mapping between collected RGB value and standard RGB value through the calibration of the X-rite Color Checker, and represented the color difference by computing ΔE in CIELab color space. This improved algorithm adopts the idea of segmentation to select the most suitable function in different intervals, and the interval of segmentation is automatically determined by the chromatic aberration standard. The experimental results show that this algorithm has high correction accuracy and this algorithm is more adaptable to photos under different lighting conditions.
Rotation Axis Focused Attention Network (RAFA-Net) for Estimating Head Pose

Head pose is a vital indicator of human attention and behavior. Therefore, automatic estimation of head pose from images is key to many applications. In this paper, we propose a novel approach for head pose estimation from a single RGB image. Many existing approaches often predict head poses by localizing facial landmarks and then solve 2D to 3D correspondence problem with a mean head model. Such approaches rely entirely on the landmark detection accuracy, an ad-hoc alignment step, and the extraneous head model. To address this drawback, we present an end-to-end deep network, which explores rotation axis (yaw, pitch and roll) focused innovative attention mechanism to capture the subtle changes in images. The mechanism uses attentional spatial pooling from a self-attention layer and learns the importance over fine-grained to coarse spatial structures and combine them to capture rich semantic information concerning a given rotation axis. The evaluation of our approach using three benchmark datasets is very competitive to state-of-the-arts, including with and without landmark-based methods. Code can be found at https://github.com/ArdhenduBehera/RAFA-Net.
BFRVSR: A Bidirectional Frame Recurrent Method for Video Super-Resolution

Video super-resolution is a challenging task. One possible solution, called the sliding window method, tries to divide the generation of high-resolution video sequences into independent subtasks. Another popular method, named the recurrent algorithm, utilizes the generated high-resolution images of previous frames to generate the high-resolution image. However, both methods have some unavoidable disadvantages. The former method usually leads to bad temporal consistency and has higher computational cost, while the latter method cannot always make full use of information contained by optical flow or any other calculated features. Thus, more investigations need to be done to explore the balance between these two methods. In this work, a bidirectional frame recurrent video super-resolution method is proposed. To be specific, reverse training is proposed that also utilizes a generated high-resolution frame to help estimate the high-resolution version of the former frame. The bidirectional recurrent method guarantees temporal consistency and also makes full use of the adjacent information due to the bidirectional training operation, while the computational cost is acceptable. Experimental results demonstrate that the bidirectional super-resolution framework gives remarkable performance and it solves time-related problems.
Limited-Anchor Deep Neural Network for Moving Object Detection

This paper proposes a new method that integrates a deep learning based object detection network into traditional background modeling to detect moving objects. The proposed method allows us to efficiently identify candidates that contain moving objects while only setting a small number of anchors in the moving area of the image through guidance from the traditional background modeling method. This paper overcomes the disadvantages of conventional background modeling methods and conventional deep learning based object detection methods in terms of dynamic backgrounds and objects' motion states.
A Novel Approach to the Implementation of Cloud-Based Quantum Programming Platforms in VR Environment

Quantum computers are becoming more and more powerful and practical each day with the perfection of the coherence time of superposition states and in terms of fidelity, error correction, power dissipation, and noise. Quantum computers have been used successfully in the solution of non-deterministic polynomial-time problems for a while. Using quantum algorithms to find ways to speed up the computing of some hard problems reveals a concept called quantum supremacy. With the improvement of quantum computers and a decrease in error rates, companies that develop quantum computers have offered cloud-based platforms to make their computers publicly available and to be programmed via the web. The cloud programming approach likewise influenced the programming of quantum computers. Here, we present that it is possible to use these platforms with VR sets, which are now becoming widespread in the field of education, and we try to introduce which tools are used in quantum cloud programming.
Approaching Quantum Entanglement Developing a Serious Game in Quantum Computing for IT Professionals

Quantum Computing (QC) has gained a constantly rising level of attention and interest in the last years from academic institutions and research teams in the industry alike. Although several serious games (SG) for QC are available to date, the specific learning needs of IT professionals are not sufficiently covered to equip them with a knowledge base on the fundamental concepts of QC. Based on an analysis of 12 current SGs in QC, we have identified the concept of Entanglement as an underrepresented learning objective and have developed a SG around Quantum Entanglement using the Greenberg-Horne-Zeilinger (GHZ) experiment as a joint project in cooperation with the IBM Quantum Ambassadors team. This paper advances the knowledge base within the field of SGs and learning across three dimensions: (1) it identifies Quantum Entanglement as underrepresented learning topic in the array of QC concepts covered by SGs, (2) provides a case study on the development of a SG in the application domain QC for a business context, and therefore (3) fundamentally adds to the understanding how and with whom such a SG is used in the advanced professional computing industry.
Two-Degree-of-Freedom Actuator for Robotic Eyes

Environment recognition is an essential task for autonomous systems. The size and weight of conventional mechanisms for robotic eyes increases because it consists of multiple actuators and links. Then, we propose a novel two-degree-of-freedom (DOF) actuator for robotic eyes, employing the basic operating principle of moving coil actuators. Since the mover of the actuator consists of two moving coils, its structure and control system can be simple. In this paper, its magnetic structure and operating principle are presented. The configuration of the robotic eye is described. The torque characteristics of the proposed two-DOF actuator are clarified by a magnetic field analysis using a three-dimensional finite element method. The analysis results show that the proposed two-DOF actuator can be independently driven by two-phase. The maximum angular acceleration is almost the same as human performance.
Correction of Systematic Error in Sub-pixel Edge Location

In order to improve the accuracy and resolution in edge location, the systematic error in Canny's sub-pixel edge detector is studied, as well as a method to compensate it is presented in this paper. The digital edge is derived from an ideal step edge through imaging and sampling. Because the high frequency part exceeding the cut off frequency of normal optical systems is not zero, for a given image sensor such as a CCD, there will be some certain aliasing from the insufficient sampling, which results in the systematic error in sub-pixel edge location. This error is periodic in each pixel and is affected by the aberration of optical system and noise. A compensation method is presented based on Multilayer-Perceptron Artificial Neural Network (MLP-ANN). The training data set is constructed by moving and tilting a straight edge randomly, the output of MLP-ANN is the compensation for the systematic error. It is shown that in high signal noise ratio images, systematic errors are main sources of edge location error. The standard deviation of location error for a straight edge was 0.019 pixels in our setup, and after compensation, the standard deviation decreased to 0.010 pixels, this can meet the requirement for most practical engineering measurements.
Post-quantum Digital-Signature Algorithms on Finite 6-Dimensional Non-commutative Algebras

There are introduced three methods for defining finite 6-dimensional associative algebras over the ground finite field GF(p), every one of which contains a set of the global right-sided units. Formulas describing the set of the global units are presented for every of the considered three algebras that contain $$p^s$$ global units, where $$s=2,3,4.$$ The algebras are used as carriers of the hidden discrete logarithm problem that is used as the base cryptographic primitive of the post-quantum digital signature algorithms.
Frequency Attention Network: Blind Noise Removal for Real Images

With outstanding feature extraction capabilities, deep convolutional neural networks (CNNs) have achieved extraordinary improvements in image denoising tasks. However, because of the difference of statistical characteristics of signal-dependent noise and signal-independent noise, it is hard to model real noise for training and blind real image denoising is still an important challenge problem. In this work we propose a method for blind image denoising that combines frequency domain analysis and attention mechanism, named frequency attention network (FAN). We adopt wavelet transform to convert images from spatial domain to frequency domain with more sparse features to utilize spectral information and structure information. For the denoising task, the objective of the neural network is to estimate the optimal solution of the wavelet coefficients of the clean image by nonlinear characteristics, which makes FAN possess good interpretability. Meanwhile, spatial and channel mechanisms are employed to enhance feature maps at different scales for capturing contextual information. Extensive experiments on the synthetic noise dataset and two real-world noise benchmarks indicate the superiority of our method over other competing methods at different noise type cases in blind image denoising.
Dense Attention Convolutional Network for Image Classification

Abstract Convolutional neural networks (CNN) have made rapid progress for a series of visual tasks, but the bottom-up convolutional feature extraction process is not qualified to mimic the human visual perception process, which takes advantages of discriminative features. Although attention modules extract the top-down discriminative features and have been widely investigated in the past few years, current attention modules interrupt the bottom-up convolutional feature extraction process. To tackle this challenge, in this paper, we introduce dense connection structure to fuse discriminative features from attention modules and convolutional features, which we term as dense attention learning . Also, to alleviate the over-fitting problem caused by rapid feature dimension growth, we propose a channel-wise attention module to compress and refine the convolutional features. Based on these strategies, we build a dense attention convolutional neural network (DA-CNN) for visual recognition. Exhaustive experiments on four challenging datasets including CIFAR-10, CIFAR-100, SVHN and ImageNet demonstrate that our DA-CNN outperforms many state-of-the-art methods. Moreover, the effectiveness of our dense attention learning and channel-wise attention module is also validated.
Risk-Aware Motion Control for Care Robots

A risk-aware motion control system is presented so that care robots can conduct human-like behaviors by changing the behavior patterns concerning environmental risk. First, a method of evaluating the environmental risk of possible collision is introduced, by measuring the narrowness of the robot-centered space. Then a way of defining and achieving robot behavior patterns through the limitation of velocity and acceleration in the motion controller is presented. Finally, a system integrating the risk evaluation module and behavior adjust module is introduced which allows human-like behaviors upon traditional indoor navigation. Experiments were conducted in a real household environment with our care robot, KUT-PCR, in which the effectiveness of the proposed approach was verified.
An Ontology-based Approach to Describe Collaborative Work by Reusing and Enriching Data From an Institutional Repository

Besides tutoring and consultancies, the development of academic and scientific documents in universities evidenced collaborative work. This paper presents an ontology-based approach to describe different modes of collaboration by reusing and enriching data from an institutional repository, from a collection of posters. The approach uses an application ontology that makes explicit the relationships among authors and posters. The paper presents a list of competency questions that are answered in natural language and by the ontology terminology. The proposed approach is of value as this offers machine-readable data to support further analysis and inference mechanisms. This paper represents a reviewed version of the described for the CEUR proceedings for the “Twelfth Latin American Workshop on New Methods of Reasoning 2019 Logic / Languages, Algorithms, New Methods of Reasoning (LANMR 2019)”.
Large-Scale Cross-Domain Few-Shot Learning

Learning classifiers for novel classes with a few training examples (shots) in a new domain is a practical problem setting. However, the two problems involved in this setting, few-shot learning (FSL) and domain adaption (DA), have only been studied separately so far. In this paper, for the first time, the problem of large-scale cross-domain few-shot learning is tackled. To overcome the dual challenges of few-shot and domain gap, we propose a novel Triplet Autoencoder (TriAE) model. The model aims to learn a latent subspace where not only transfer learning from the source classes to the novel classes occurs, but also domain alignment takes place. An efficient model optimization algorithm is formulated, followed by rigorous theoretical analysis. Extensive experiments on two large-scale cross-domain datasets show that our TriAE model outperforms the state-of-the-art FSL and domain adaptation models, as well as their naive combinations. Interestingly, under the conventional large-scale FSL setting, our TriAE model also outperforms existing FSL methods by significantly margins, indicating that domain gaps are universally present.
A Novel Image Registration Method based on Pre-registration and SURF

This paper proposed a novel image registration method based on pre-registration and SURF. The proposed pre-registration method based on clustering FAST feature points included three steps. Firstly, the FAST feature points of the adjacent images were detected and down-sampled to obtain most of the feature points located in the overlapping part. Then, the DBSCAN clustering was performed on FAST feature points to get the feature points' clusters in the largest overlap area. Finally, according to the coordinates of the boundary points in the cluster point set, the range of the area where the feature points are concentrated can be obtained. After delineating the feature points' concentrated area, SURF was used for image registration. Experimental results show that, compared with directly using SURF, the proposed method cost less time, and had higher correct matching rate.
Medical Image Processing Using Soft Computing Techniques and Mathematical Morphology

Medical image processing techniques have revolutionized the visual depictions of within a body medicinal examination and visual description of certain organs or tissues. Medical imaging exploring brain anatomy and functions to analyze brain injuries have always been a complex task as there are large amount of datasets, which require computerized methods to extract the useful information. This paper presents the clustering-based segmentation approach using soft computing techniques with the aid of morphology to segment the region of interest from the MRI images. This work proposes a method of processing the MRI images using Particle Swarm Optimization (PSO)-based clustering approach for the segmentation of brain tissues, as to avoid the limitation of initial cluster definition and to improve the accuracy in comparison with the Fuzzy C Means (FCM)-based segmentation technique. The proposed method has been validated and accuracy is calculated as performance evaluation parameter, with compared to ground truth of given dataset in different images.
Enhanced Multistage RSA Encryption Model

Digital data amounts are expanding quickly every day over the Internet. Security plays an essential role in the advancement of communication system, particularly with confidential material transmitted over a network because of the continuous availability of digital data and effective attackers try to access these data cryptography becomes one of the most major fields used, an essential and necessary field for achieving a high level of protection between distinct individuals. Efficient and new editions of cryptography algorithms can help decrease security risks, any type of data has its own confidentiality, so new algorithms should be used to protect confidential data toward unauthorized access. After reviewing the RSA and attacking it, it would appear that a new model should be improved to mitigate this attacks and improve the security of the RSA algorithm. In this research increasing the complexity and search space of RSA algorithm against brute force attack in addition to security enhancement was satisfied by applying four cases with using different cryptography algorithms. This four cases included case1: enhanced the security of RSA by using Optimal Asymmetric Encryption Padding (OAEP), case2: combining of the two most important algorithms RSA and Diffie-Hellman (D-H), case3: for increasing complexity and obtaining high level of security the two above cases (case1 & case2)were concatenated, finally for most complexity and obtained highest security level with increasing search space of RSA case4 was applied. Case4: contained implementation of case3 in addition to apply new level of security by adding another cryptography algorithm called HiSea algorithm. The results of using multiple cryptography algorithm in each case of the above four cases respectively improved security level by increased the complexity and key search space that lead to protect the security goals against the attackers. Case4 is the best case because it provides most efficient complex and accurate encryption system that used the processing of encryption data multiple times using different strong algorithms.
Effective Factors Analysis on Camera Calibration

This paper conducted some experiments to analyze three effect factors on camera calibration, which includes light intensity variation, chessboard planarity and image number. Firstly, three factors are discussed to quantify their effect on calibration accuracy. For convenience of analysis, camera calibration process and methods are explained in detail. Then, error model is constructed by deriving from perspective equation. Finally, some related experiments are conducted under different conditions. Experiments show that light intensity has less effect on feature extraction of chessboard corner. Therefore, its effect could be neglected on calculation accuracy. On the contrary, the world coordinate of chessboard corner is one of main effect factors on camera calibration. In other words, more attentions should be paid to chessboard planarity for improving calibration accuracy. Furthermore, it can be found that position of main point inevitably varies with the increasing of image number and effective focal length. Experiment results point out that the calculation accuracy and efficiency of inner parameters will become more ideal under condition of image number of 17~24.