Reviewerly/exHarmony huggingface dataset:
A Hybrid, Coupled Approach to the Continuous-Discrete Kalman Filter

In this letter we present a novel approach to continuous-discrete (CD) Kalman filtering. Unlike the EKF or other hybrid UKF-EKF filters, the novel approach does not require direct calculation of system Jacobians and instead uses unscented transforms (UTs) to extract a pair of matrices, each made up of a linear combination of derivatives (with respect to the state), that are used in its place. More specifically, they are used (1) in parts of the filtering process, and (2) in the linearly implicit numerical integration scheme of the filter's state propagation stage. Extracting these matrices from the filter's UTs and using them in both the filtering process and model simulation, or what we refer to as coupling the filter to model simulation, avoids having to calculate further function evaluations for standard implicit methods, making the process of state estimation for stiff systems more efficient. Another benefit of the proposed approach is that it offers UKF accuracy levels but improved numerical stability over the UKF. This is because it uses symmetric and positive-definite (PD) representations of the state covariance propagation and measurement update equations.
A spatial minimum spanning tree filter

Abstract It is well-known that the minimum spanning tree (MST) is widely used in image segment, edge-preserving filtering, and stereo matching. However, the non-local (NL) filter based on the MST generally results in overly smooth images, since it ignores spatial affinity. In this paper, we propose a new spatial minimum spanning tree filter (SMSTF) to improve the performance of the NL filter by designing a spatial MST to avoid over-smoothing problems, by introducing recursive techniques to implement the filtering process. The SMSTF has the advantages that: (1) the kernel of our filter considers spatial affinity and similarity of intensity; (2) The size of the filter kernel is the entire image domain; (3) the complexity of the SMSTF is linear to the number of image pixels. For these reasons, our filter achieves excellent edge-preserving results. Extensive experiments demonstrate the versatility of the proposed method in a variety of image processing and computer vision tasks, including edge-preserving smoothing, stylization, colorization, and stereo matching.
Semi-supervised advancement of underwater visual quality

Abstract In the underwater environment, the backscattering and attenuation of wavelength-dependent light degrade the quality of underwater vision. Low-quality underwater vision will reduce the accuracy of underwater robot visual navigation and pattern recognition. A novel semi-supervised deep convolutional neural network composed of a supervised learning branch and an unsupervised learning branch is proposed herein to improve underwater visual quality with poor visibility in real time. The network is constrained by a supervised loss function consisting of mean square, underwater index, and adversarial loss. The supervised branch serves as the baseline of the image enhancement algorithm to learn the basic feature information of the images and restore the original colors. The unsupervised learning branch, which makes the generated images more realistic and reduces reliance on the quality of the simulation model of synthetic data, applies underwater dark channel prior loss and total variation loss to learn the feature domain information of real images. Experiments show that the results of the proposed method show less color shift, lower fogging and blurring, and more pleasing high-quality vision. The enhanced images can extract more useful feature information, which is promising in the online visual navigation of underwater robots.
Utilization of Nursing Education Progressive Web Application (NEPWA) Media in an Education and Health Promotion Course Using Gagne’s Model of Instructional Design on Nursing Students: Quantitative Research and Development Study

Previous studies have proven that web-based learning media that offer interesting features with the learning management system concept could support the learning processes of nursing students. Nonetheless, it is still necessary to conduct further research on its potential as an information media that supports learning using 1 of the mobile learning methods.This study aims to develop and use the Nursing Education Progressive Web Application (NEPWA) media in an education and health promotion course for nursing students.This is a research and development study aimed at developing the NEPWA media using the Analyze, Design, Develop, Implement, and Evaluate approach and a quantitative research with descriptive and pre-experimental 1-group pretest-posttest design conducted in the Study Program of Nursing Sciences, Faculty of Health Sciences, Muhammadiyah University of Surakarta. A total of 39 nursing students in their second year of undergraduate studies participated in this study. A pretest-posttest design was used to measure any changes in the dependent variable, whereas a posttest design was used to measure any changes in the independent variables.After using the NEPWA media, there was a significant increase in the student knowledge variable (N=39; knowledge: P<.001; 95% CI 23.88-33.14). In terms of student satisfaction with the learning process using Gagne's model of instructional design, most of the students were satisfied, with a mean score of ≥3. In addition, the results of the measurement using the System Usability Scale on the NEPWA media showed that NEPWA has good usability and it is acceptable by users, with a mean score of 72.24 (SD 8.54).The NEPWA media can be accepted by users and has good usability, and this media is designed to enhance student knowledge.
Accurate Prediction of Streaming Video Traffic in TCP/IP Networks using DPI and Deep Learning

Video share of the Internet traffic is increasing day by day. This includes streaming on the go to/from mobile devices. These trends necessitate dynamic and robust resource allocation at Internet Exchange Point level to provide good quality of services to mobile video users. Any effective solution to this problem requires accurate predictions of the video traffic coming from or delivered to mobile devices. In this paper, we propose a framework to correctly identify and accurately predict the live streaming and video traffic. Deep packet inspection is used to identify the 23 most common live streaming and video traffic protocols. Subsequently Long Short-Term Memory neural network is used to predict the live streaming and video traffic over a prediction horizon of 6 hours with an average accuracy of 97.24% thus outperforming previous frameworks in both the accuracy and the prediction horizon. This technique can be used as a baseline towards a more effective application of traffic engineering techniques.
Image Analysis System of Intelligent Smart Home Based on VR

With the increasing aging today, the rich life experience has become a topic that people are urgently concerned about. This research mainly discusses the research and application of VR-based smart home image analysis system. This research combines the intelligent design and implementation of the image analysis system of VR technology. The system uses 2D image processing, segmentation, and recognition to filter out information that is of interest to the image, which is helpful for a reasonable plan for smart homes. During the experiment, a mixed model was used to model the background scene, the background difference method was used to detect the moving target in the image, and then the face detection of the moving target was performed. Through such simple real-time image processing, the necessity of real-time video monitoring is realized. After that, the abnormal image data can be saved to provide effective evidence, and the system will issue a warning after detecting the abnormality, which can effectively monitor specific areas such as home. 110 frames of video clips of people actually walking indoors are used for face detection. Experimentally, the acquisition speed of the VR technology camera is 24 frames per second, and the resolution is 420*250. The research results show that image processing is an indispensable part of the intelligent process of video surveillance. It can also be said that the image processing technology of VR technology is more suitable for the smart and suitable home.
Optimal Path Planning for Robot Based on Ant Colony Algorithm

With the development of computer technology and intelligent computing, robot technology also gets more and more attention, in view of this, the robot path planning problem is also particularly important. In this paper, the optimal path planning for robot based on ant colony algorithm is proposed by studying the related literature and important techniques about robot path planning in China and abroad. This paper mainly considers that under the condition of sensing the current environment, the robot dynamically avoids the obstacles and meets the requirements of the least time and the minimum walking path. On the basis of introducing the robot path planning problem, the mathematical model is established for the ant colony algorithm, and the experiment is conducted through the analogue simulation. The experimental results show that the model can intelligently choose a path for the robot with dynamic obstacle avoidance, having the shortest time and shorter distance, and has a certain contribution to the intelligent development for the robot.
Metacognitive experience on Raven’s matrices versus insight problems

Abstract Participants rated Intuition, Suddenness, Pleasure, and Certainty accompanying their solutions to items of a popular fluid intelligence test – Raven’s Advanced Progressive Matrices (RAPM) – that varied from easy (around 80% correct) to difficult (around 20% correct). The same ratings were collected from four insight problems interleaved with RAPM. Suddenness and Certainty substantially decreased from easy to difficult matrices (Pleasure strongly overlapped with Certainty). In easy matrices, subjective experience matched that observed during insight problems, suggesting the highly fluent processing resulting in vivid and univocal solutions. By contrast, processing difficult matrices seemed to involve effortful incremental combination of complex information that yielded uncertain outcomes, resembling full-blown analytic problems. Only Intuition, generally rated low, was unaffected by RAPM difficulty. These results suggest that RAPM constitutes a heterogeneous test, with easy vs. difficult items involving relatively distinct types of processing. This novel knowledge can help in understanding the processes underlying solving Raven’s matrices. The study also contributes to the understanding of the validity of subjective ratings as measures of metacognition.
A Machine Learning Approach to Reduce Dimensional Space in Large Datasets

Large datasets computing is a research problem as well as a huge challenge due to massive amounts of data that are mined and crunched in order to successfully analyze these massive datasets because they constitute a valuable source of information over different and cross-folded domains, and therefore it represents an irreplaceable opportunity. Hence, the increasing number of environments that use data-intensive computations need more complex calculations than the ones applied to grid-based infrastructures. In this way, this paper analyzes the most commonly used algorithms regarding to this complex problem of handling large datasets whose part of research efforts are focused on reducing dimensional space. Consequently, we present a novel machine learning method that reduces dimensional space in large datasets. This approach is carried out by developing different phases: merging all datasets as a huge one, performing the Extract, Transform and Load (ETL) process, applying the Principal Component Analysis (PCA) algorithm to machine learning techniques, and finally displaying the data results by means of dashboards. The major contribution in this paper is the development of a novel architecture divided into five phases that presents an hybrid method of machine learning for reducing dimensional space in large datasets. In order to verify the correctness of our proposal, we have presented a case study with a complex dataset, specifically an epileptic seizure recognition database. The experiments carried out are very promising since they present very encouraging results to be applied to a great number of different domains.
Face Gender Classification Based on Convolutional Neural Networks

Gender is the most basic information on human beings. It is of great significance in the field of face recognition. However, due to factors such as lighting, poses, and expressions, gender classification at this stage has the problems of low accuracy and slow training speed. In this paper, various variants of convolutional neural networks are used to deal with gender classification tasks, namely VGG16, Inception V3 and ResNet50. Besides, the whole process from dataset selection, dataset processing to face gender classification is described. The experimental results show that face gender classification based on convolutional neural networks can improve training speed and classification accuracy, and the highest classification accuracy can reach 95.10%.
An efficient kernel-based feature extraction using a pull–push method

Supervised dimensionality reduction methods using nonlinear mappings for pattern recognition tasks are more appropriate for nonlinearly distributed data. Generally, for most algorithms, these samples (called hard samples) located at the edge or in other classes influence the performance of the proposed methods. In this study, the large margin nearest neighbor (LMNN) and weighted local modularity (WLM) in the complex network are introduced to deal with these hard samples to push and pull them rapidly toward the center of the class, and the samples with the same label shrink, as a whole, into the center of the class. A novel feature extraction method named KWLM–LMNN, which uses the kernel trick and combining WLM with LMNN, is proposed. Comparative experiments with state-of-the-art feature extraction methods demonstrate the effectiveness of the proposed method.
Two is Better Than One. Improved Attention Guiding in AR by Combining Techniques

Search tasks in production environments have been shown to be significantly improved by attention guiding techniques in augmented reality (AR). In mobile use cases, head-mounted displays support context-sensitive and flexible search tasks. To improve search time and navigation efficiency, first concepts like attention funnel and spherical wave based guidance techniques have been tested successfully in comparison to simple arrow navigation. Existing research indicates that for two-dimensional interfaces, a differentiation into coarse and fine navigation will result in further performance improvements. In this article, a laboratory study is presented with a stereoscopic head-mounted display that examines whether an additional arrow guiding technique for coarse navigation could improve both search time and efficiency of existing techniques. Results show objectively superior performance for one of the combined techniques in comparison to the unimodal attention guiding techniques and subjective preference by the participants. Further research should investigate the performance in a real environment.
Generalized objects in the system with dispersed knowledge

The inference process based on a set of local decision tables is considered in the article. Determining global decisions in such a situation is a complex and time-consuming task. The aim of the presented approach is to simplify this process by significantly reducing the size of local decision tables and, at the same time, maintaining the quality of decisions made. The research objective is the application of generalized objects with respect to the indiscernibility relation. When we use the generalized objects only the relevant and consistent knowledge remains in tables. In addition, the use of generalized objects for local tables results in a significant reduction in the number of objects in the tables. Such a change has a huge impact on the system’s construction for dispersed data. The paper introduces a definition of the generalized objects that are suitable for quantitative data. In addition, a definition of the generalized objects generated with the accuracy expressed by the parameter, which are appropriate for qualitative data, is given. It was experimentally tested that the use of generalized objects significantly reduces the number of objects in local tables, up to 80% or even 90% percent of the original table. In addition, it was shown that the use of generalized objects for local tables with large number of attributes gives comparable quality of classification to the dispersed system using full objects. Moreover, it was depicted that the quality of classification obtained for such data and the dispersed system with the generalized objects is better than the quality that are obtained for full objects without using the dispersed system.
Few-labeled visual recognition for self-driving using multi-view visual-semantic representation

For a car self-driving system, it is vital to accurately recognize the objects on the road. This is often achieved by analyzing the images captured by various cameras. However, in real applications, we often have limited number of labeled images. Besides, it is very expensive to manually annotate objects in images. It is hard to learn reliable classifiers when only few-labeled images are available. To alleviate these problems, multi-view information is used. However, only using visual information is not enough for reliable classification. Besides, we often have limited images with labels. To cope with these problems, in this paper, we propose a novel multi-view visual-semantic representation method for few-labeled visual recognition (MV2S). We make use of the state-of-the-art deep convolutional neural networks by viewing them as different views to extract semantic representations of images. This is achieved by using the learned deep convolutional neural networks to make predictions of the semantics of images. Both the visual and semantic representations of images are then used to predict the categories of images by combining the predictions of multi-views with visual and semantic consistency constraints. Experiments on four public available datasets prove the effectiveness of the proposed MV2S method.
End-to-End Learning-based Amplify-and-Forward Relay Networks using Autoencoders

In this work, we study an end-to-end deep learning (DL) based constellation design for the amplify-and-forward (AF) relaying network. Firstly, we study the one-way (OW) and two-way (TW) AF relay networks as an autoencoder by utilizing a single channel to transmit the desired bits, whilst operating under Rayleigh fading channels. As a result of optimal constellation design via end-to-end DL-based framework, we achieve a performance gain of 4 dB and 1.2 dB at 10 dB average signal-to-noise ratio (SNR) over conventional OWAF and TWAF relay networks, respectively. Secondly, by adding redundant bits at the transmitter, we jointly design an end-to-end DL-based coding and modulation scheme for block fading Rayleigh channels. This leads to DL-based coding and modulation, and DL-based differential coding and modulation, similar to the coded modulation and differential coded modulation in conventional networks, depending upon the presence of channel state information knowledge at the receivers. Thus, we propose an end-to-end DL-based data-driven frameworks for differential coded modulation in OWAF and coded modulation in TWAF relay networks. Lastly, we show that at 20 dB average SNR, our proposed methods (DL based differential coded modulated OWAF and DL-based coded modulated TWAF) achieve a gain of 4 dB and 4.8 dB, over conventional OWAF and TWAF relay networks employing Hamming codes with same rates.
Experimenting with Beta Distributions for Approximating Hammocks’ Reliability

It is a well-known fact that, in general, the combinatorial problem of finding the reliability polynomial of a two-terminal network belongs to the class of $$ \# P $$ -complete problems. In particular, hammock (aka brick-wall) networks are particular two-terminal networks introduced by Moore and Shannon in 1956. Rather unexpectedly, hammock networks seem to be ubiquitous, spanning from biology (neural cytoskeleton) to quantum computing (layout of quantum gates). Because computing exactly the reliability of large hammock networks seems unlikely (even in the long term), the alternatives we are facing fall under approximation techniques using: (i) simpler ‘equivalent’ networks; (ii) lower and upper bounds; (iii) estimates of (some of) the coefficients; (iv) interpolation (e.g., Bézier, Hermite, Lagrange, splines, etc.); and (v) combinations of (some of) the approaches mentioned above. In this paper we shall advocate—for the first time ever—for an approximation based on an ‘equivalent’ statistical distribution. In particular, we shall argue that as counting (lattice paths) is at the heart of the problem of estimating reliability for such networks, the binomial distribution might be a (very) good starting point. As the number of alternatives (lattice paths) gets larger and larger, a continuous approximation like the normal distribution naturally comes to mind. Still, as the number of alternatives (lattice paths) becomes humongous very quickly, more accurate and flexible approximations might be needed. That is why we put forward the beta distribution (as it can match the binomial distribution), and we use it in conjunction with a few exact coefficients (which help fitting the tails) to approximate the reliability of hammock networks.
Securely and Efficiently Computing the Hermite Normal Form of Integer Matrices via Cloud Computing

The prevalence of cloud computing greatly promotes the traditional computing paradigm. With the assistance of a cloud, the light-weight device can achieve computation-intensive tasks which may not be done on its own. While, computing the Hermite Normal Form (HNF), which is a standard form of integer matrices, not only is inescapable when solving the linear system of equations over integers, but also has lots of applications in many other fields, such as integer programming and lattice-based cryptography. However, the fast blowup phenomenon of intermediate numbers makes the HNF computation time-consuming. In this paper, we initialize the study of the cloud-assisted HNF computation and design an efficient outsourcing algorithm that enables the resource-constrained client to securely delegate this heavy computation to a resource-abundant yet maybe untrusted cloud server. The main idea involved in our algorithm is a novel matrix encryption method based on random permutation, unimodular matrix transformation and triangular matrix transformation, which makes our algorithm protect the client's input/output information with the one-way notion and enable the client to detect the cloud's deception with the optimal probability 1. Besides, rigorous theoretical analysis and extensive experimental evaluation validate the efficiency and the practical performance of our design, and the substantial client-side savings are remarkable as the problem size increases.