Reviewerly/exHarmony huggingface dataset:
An Approach to Image Matching Based on SIFT and ORB Algorithms

A four-stage approach to the matching of medical images based on the use of SIFT and ORB algorithms has been considered. At the first stage, keypoints and their corresponding descriptors are identified using the SIFT or ORB algorithm for a certain pair of images. At the second stage, the identified descriptors corresponding to keypoints have been clustered in order to determine the optimal number of clusters and, if necessary, identify target clusters among them. In this case, clustering can be performed using some algorithm which allows to determine the centroids or medoids of the clusters. It is proposed to exclude from further consideration those keypoints whose descriptors are significantly removed from the centroids (medoids) of their clusters. At the third and fourth stages, the sequential identification of “good” matches based on the Lowe’s ratio test and the homography method for the remaining keypoints, for example, of target clusters with the rejection of matches which can be classified as noise has been realized. A practical example which realizes the matching of images from the field of medical diagnostics and confirms the effectiveness of the proposed approach has been given.
Learning Depth Information in Layout for Sketch Generation from Scene Graph

The research of generating images from scene graphs has become a hot topic, benefiting from the success of Generative Adversarial Network (GAN). Previous works in this field are still challenged by the complexity of texture pattern and object structure in images. In fact, it is more desirable to generate sketches directly, rather than image synthesis from scene graphs. The sketch is an abstract and iconic image representation, which describes the object structure and scene layout well but ignoring complex texture patterns, leading to a more reasonable generation task. Furthermore, there are two real-world tasks can illustrate the importance of this problem: Courtroom sketch and Crime Scene sketch, which are less studied before. To this end, we, for the first time, study the task of generating sketches from scene graphs. Essentially, the main novelties are two folds. First, a new sketch generation framework is developed which is trained with both newly designed perceptual loss and adversarial loss. Second, we propose a new layout encoding block to learn the depth information for each object. Extensive experiments on several widely used datasets validate the good performance of the proposed approach.
Application research of digital assembly system of marine turbocharger based on VR/AR

VR and AR are new computer technologies for creating simulated environments in the field of the shipping industry. Therefore, it is of great significance to study the application of VR and AR by 5G enabled technology with large bandwidth and low delay in ship assembly. An application framework of the digital assembly system of a marine turbocharger based on VR/AR is presented and its operation mechanism is analyzed in his paper. The application framework of the digital assembly system of turbochargers based on VR/AR mainly consists of five parts: the physical layer, data layer, system underlying layer, system layer, and application layer. System design and implementation and implementation effect of the digital assembly system of the marine turbocharger based on VR/AR are described in detail. The operation mode of the digital assembly simulation system of a turbocharger based on VR/AR is studied to improve the production capacity of turbochargers assembly.
What’s the Point? A Study of Full-Stop Use in Text Messages in Varying Emotional Contexts

Because of the pervasiveness of the text messaging medium, further understanding of meaning-making practices in text messages is desirable. This study offers new knowledge of the potential uses and meanings of the full-stop in text messages. Although the full-stop has become infamous for its potential negative connotations in text messages, few studies have evaluated uses of the full-stop in varied emotional contexts. For this study, text message Discourse Completion Tasks (DCTs) detailing different emotional contexts were given to undergraduate students. The uses of full-stops in the text message responses elicited with the DCTs were evaluated via two methods of analysis. First, a basic content analysis was conducted for the purpose of understanding the relative uses of the full-stop in the differing emotional contexts. Second, a semiotic analysis was conducted to understand more fully how exactly the elicited full-stops were deployed and what meanings they might convey. It was concluded that the full-stop is not always a device with a negative connotation, and that, in certain circumstances, the full-stop may be seen as more appropriate than other punctuation marks. It may even be seen as a conveyer of formality, seriousness, or earnestness. The study offers practical knowledge of how a certain typographical device is used, as well as the more theoretical knowledge that punctuation devices, like words, can possess a certain semiotic elasticity: their uses and meanings can vary, depending on the contexts in which they are deployed and the people who write and read them.
Variational Approximation for Extended Target Tracking in Clutter with Random Matrix

Random matrix is a simple and efficient method for extended target tracking. In this paper, based on variational Bayesian approximation, we present a Gamma Gaussian inverse Wishart filter for the extended target tracking in clutter with unknown measurement rate. A new probability data association method based on random matrices is proposed to handle the measurement origin uncertainty. The recursive estimation of the kinematic state, extension and unknown parameters are derived using variational Bayesian inference. Finally, the simulation results verify the validity of the proposed method.
Image Segmentation Module Development for Image Processing Learning Mobile Application

This paper focuses in the development of an image segmentation module for an image processing learning application. This image segmentation module consists of edge detection sub-module and region-based segmentation sub-module. The Graphical User Interface (GUI) is developed by using the Android Studio software. The application is provided with various segmentation techniques, automatic and adjustable parameters, together with brief explanations for each image processing function to improve the learning efficiency of the users. The performance analysis is evaluated through data collection from survey conducted among university students attending formal Image Processing course. The surveys comprise of the market test and performance review surveys. It can be concluded that the understanding level among students increased 133.33% for extremely understand level, and 20% in-creased for moderately understand level. Furthermore, the number of students who did not understand has been decreased 100%. Therefore, 100% of respondents with formal Image Processing knowledge agreed that their understanding increased with the assistance of this application in their conventional face-to-face Image Processing learning.
A Method for Measuring Displacement and Strain Around a Crack of Rubber Sheets Using Digital Image Correlation

This study establishes a method for measuring displacement and strain of rubber materials with large and fast deformations using digital image correlation. Using the proposed method, the behavior of the crack tip, which is important for elucidating the growth of cracks is evaluated. A tensile load is applied to the rubber test piece containing the initial crack, and the state is photographed with a digital camera. The results show that oscillating variations displacement and strain rates near the crack from the start of crack growth to fracture are observed.
A Preliminary Study on Wav2Vec 2.0 Embeddings for Text-to-Speech

Wav2Vec 2.0 (W2V), a self-supervised speech representation trained with massive unlabeled speech data, showed promising results on Automatic Speech Recognition (ASR). In spite of several evidences showing that W2V can generate unique acoustic features, it has been rarely utilized in Text-to-Speech (TTS) task. In this paper, we adapt W2V embed dings to TTS as feature vectors. Our TTS model consists of two components: Text2Vec, which converts a character-level text sequence into W2V embeddings, and GAN-based vocoder, which decodes a W2V embedding sequence to waveform signals. From the experiments, we observe that W2V embeddings have considerable potential as acoustic features for TTS.
Toward Improving the Fuzzy KNN Algorithm Based on Takagi–Sugeno Fuzzy Inference System

In this paper we present a new approach in order to improve the performance of the Fuzzy K-Nearest Neighbor algorithm (Fuzzy KNN algorithm). We propose to use a Takagi–Sugeno Fuzzy Inference System with the Fuzzy KNN algorithm to improve classification accuracy. Also, we have used different measures to calculate the distance between the neighbors and the vector to be classified, such as the: Euclidean, Hamming, cosine similarity and city block distances. These distances represent the inputs for the Takagi–Sugeno Fuzzy Inference System. Simulation results with a classification problem show the potential of the proposed approach.
Information and Digital Culture for Future Engineers as a Factor of Socialization

The purpose of the article is to study the formation of information and digital culture for future engineers as a factor of socialization. The article is based on the analysis of documents and research on digital culture. The relevance of the problem under study is revealed. The content of the concept &#x201C;information and digital culture&#x201D; is determined, its definition is given. Methodological approaches are highlighted. The main ways of forming the information and digital culture of future engineers as a factor of socialization are determined. The main research method is the analysis of scientific works devoted to the information and digital culture of the individual, and digitalization of education. We defined the methods as a component of the structure of the model for the formation of the information and digital culture of future engineers. They are theoretical, empirical, technological, information-technological, reflexive. The design of methodological provisions is based on the principles of cybernetic, informational, activity-based and cultural approaches. Among the most effective technologies for educating the information-digital culture of future engineers there are technologies of moderation, deliberation, a pedagogical workshop, etc. The project method, modular technology belong to the list. The features of the professional activity of an engineer are revealed in the modern digital world. The presented technologies, methods, and techniques, allow to effectively carry out their stage-by-stage implementation and the formation of the information and digital culture of the future engineer. The upbringing of this culture is possible on the basis of innovative technologies, highlighted methods, and techniques. The information and digital culture of a future engineer is a systematic and consistent implementation of cultural values in a digital environment. This process requires the special methodology for organizing professional training of future specialists.
Waypoint: Online Semi-automatic Vehicle Occupancy Data Collection System

Transportation route planning requires different kinds of data, one of them being passenger traffic data. Collecting this data is essential in deciding how to adjust the number of public transport vehicles in a route. Most methods used to gather this, however, involves manual surveys or the use of automatic passenger counting (APC) systems which may not be either readily available or fully reliable in the case of computer-vision (CV) based APCs. To save resources, we developed a web-based semi-automatic video annotation system that utilizes GPS traces to find points-of-interest in the videos to reduce the total footage time needed to be reviewed by annotators while avoiding the pitfalls of a CV-based system. Test results show that it was possible to reduce the footage time to around 14.8% of the original dataset. The annotation results show that human annotators counted around 5.5 people per splice compared to the CV algorithm's results of around 2.46 per splice. Annotators also provided data regarding boarding and alighting with averages of 0.34 and 0.13 per splice. The reliability of the annotators was also seen by checking the standard deviation of the annotations with the 3 parameters having small standard deviations of around 1.31 (people count), 0.13 (boarding), and 0.06 (alighting), proving the reliability of the provided data. The system performance mostly shows that it can reliably reduce footage time that needs to be reviewed while providing reliable annotations of passenger traffic data.
Online Least-Squares Learning for Monopulse Angle Tracking and Waveform Estimation

Monopulse technique is the most widely-adopted method for angle estimation in radar systems, whereas performance improvement is required. To that end, a monopulse angle tracking algorithm named online least-squares learning (OLSL), which can also provide waveform estimation, is proposed in this paper. By establishing a least-squares based joint optimization problem of the target angle and the waveform, OLSL fully exploits the previous data for performance improvement. The estimate is updated in an online manner for acceleration. The memory scheme is further introduced to avoid loss of accuracy in the case of time-varying angles. Compared to conventional mono-pulse estimation, OLSL only requires 4 additional real-number calculations (2 additions and 2 multiplications) and the storage of 2 real-numbers, but enjoys remarkably improved accuracy and robustness against outliers. Moreover, the proposed algorithm can be simply applied to 2D angle estimation, and is compatible with various existing amplitude-comparison monopulse methods. Simulation results verify its effectiveness and superiority.
Research on target tracking algorithm based on mean shift with adaptive bandwidth

The traditional mean shift algorithm used fixed kernels or symmetric kernel function, which will cause the target tracking lost or failure. The target tracking algorithm based on mean shift with adaptive bandwidth was proposed. Firstly, the signed distance constraint function was introduced to produce the anisotropic kernel function based on signed distance kernel function. This anisotropic kernel function satisfies that the value of the region function outside the target is zero, which provides accurate tracking window for the target tracking. Secondly, calculate the mean shift window center of anisotropic kernel function template, the theory basis is the sum of vector weights from the sample point in the tracking window to the center point is zero. Thirdly, anisotropic kernel function templates adaptive update implementation by similarity threshold to limit the change of the template between two sequential pictures, so as to realize real-time precise tracking. Finally, the contrast experimental results show that our algorithm has good accuracy and high real time.
From Tokenization to Self-Supervision: Building a High-Performance Information Extraction System for Chemical Reactions in Patents

Chemical reactions and experimental conditions are fundamental information for chemical research and pharmaceutical applications. However, the latest information of chemical reactions is usually embedded in the free text of patents. The rapidly accumulating chemical patents urge automatic tools based on natural language processing (NLP) techniques for efficient and accurate information extraction. This work describes the participation of the Melax Tech team in the CLEF 2020-ChEMU Task of Chemical Reaction Extraction from Patent. The task consisted of two subtasks: (1) named entity recognition to identify compounds and different semantic roles in the chemical reaction and (2) event extraction to identify event triggers of chemical reaction and their relations with the semantic roles recognized in subtask 1. To build an end-to-end system with high performance, multiple strategies tailored to chemical patents were applied and evaluated, ranging from optimizing the tokenization, pre-training patent language models based on self-supervision, to domain knowledge-based rules. Our hybrid approaches combining different strategies achieved state-of-the-art results in both subtasks, with the top-ranked F1 of 0.957 for entity recognition and the top-ranked F1 of 0.9536 for event extraction, indicating that the proposed approaches are promising.
Hierarchical classification and visualization with multiple feature ranking criteria

Hierarchical classification is consistent with human cognitive thinking mode and easy to understand. In the process of classification, the feature ranking method has a great impact on the classification result and convergence rate. In this paper, a hierarchical classification method with multiple feature ranking criteria is proposed, including Least Number of Overlapping Interval Samples (LNOIS) method, Maximum Average Distance (MAD) method, Minimum OTSU-MSE (MOTSU-MSE) method, etc. The proposed method is intuitive and concise without adjusting the specific super parameter. To enhance the interpretability of this method, a visual system is designed based on JavaScript programming language. The method is applied to the recognition of human daily behavior, and effective features are extracted and filtered according to the characteristics of signals. The hierarchical classification model is trained based on OTSU-MSE method, and 93.06% F1 score is obtained.
Introduction

In outdoor applications, there is no escape from complex illumination such as shadows, highlight, or dynamic lighting during a day and bad weather conditions with poor visibility such as rain, snow, or fog. These wide ranges of illumination and weather conditions usually change the pixel value distribution of images, which affects the basis of robot vision algorithms and influences some basic capabilities of a robot such as vSLAM, autonomous navigation, visual servoing, and environmental understanding. In this chapter, we first analyze how complex illumination and bad weather conditions affect images from the perspective of atmospheric physics and imaging optics. We then summarize and review the basic principles of color matching function and scene reproduction. We also summarize the key technologies for all-weather robot vision including lights, scattering transmission, surface reflection, and digital camera imaging modeling. Finally, we outline the main contents and structure of this book.
Enhanced Dual Adversarial Network for Real Image Noise Removal and Generation using Edge Loss Function

Many methods have been proposed to address the real noise, they suffer from restoring the edge regions appropriately. Because most convolutional neural network-based denoising methods capture noise characteristics through pixel loss that only detects contaminated pixels, high frequency components cannot be considered. This causes blurs and artifacts on edge regions which has the high frequency component. In this paper, we apply an edge loss function to the dual adversarial network to deal with this issue. Using the edge loss and the pixel loss together, the network has been improved to restore not only the actual intensity but also the edges effectively.
Jordanian Higher Basic Stage Students’ uses of the Social Networking Site (Facebook) as a Mediator Assistant in Their Learning

It has become possible to adopt a number of modern technologies that help develop learning methods in the study environment, which may help in refining the student’s skill in accessing and using information in the classroom. Among these modern educational technologies are social networks, smart phones and Internet communication software such as Skype. Facebook is the largest social networking site in the world, with more than 500 million subscribers. One of the features of Facebook is that it contributes to downloading video and audio files in school lessons. In addition, Facebook helps in conducting open exams in order to help the student think about finding a solution. In addition, Facebook contributes to dividing students into groups in order to enhance the spirit of teamwork among students.
Improvising Inside a House of Cards: New performance and music-making through a collective networked instrument

This article presents experiences and reflections related to performing improvised, live processed electroacoustic music within a context of networked music performance. The musical interaction is performed through a new collective networked instrument, and we report how the ensemble ‘Magnify the Sound’, consisting of two of the authors of this article, meets the instrument in different networked performance situations, and how this is related to the affordance of the instrument. In our performances the network is inherent to our artistic practice, and we experience a phenomenological and somatic transformation in our roles as musicians, from individual instrumentality to shared instrumentality. The instrument invites new forms of music-making and contributes in fundamental ways to the ensemble’s musical communication and artistic expression. In the present article we outline our methods of working artistically with the networked instrument, and we point at some artistic results. We then discuss how the collective instrument has facilitated new performance and musical practice within the network.
The Influence of Theme-Based Language Teaching toward Indonesian Students’ Reading Comprehension

Reading is one of the essential skills in English language learning. In the Indonesian context, reading has been one of the essential subjects to be taught among schools and universities since English is one of the compulsory requirements for graduation. Theme-based Language Teaching (TBLT) is a prominent approach used by the teacher and the students to understand the reading. This study aimed at examining the influence of TBLT on the students' reading comprehension. This research used quantitative research with an experimental study. The study selected participants from Madrasah Aliyah (MA) NW Narmada, Indonesia. The study recruited 20 students taken from the second grade of MA NW Narmada. Pre- and post-proficiency tests were used to assemble data of the study. To analyze data, this study employed a T-test to determine the significant result. The result indicated that the pretest score achieved 71.12 and the posttest value reached 89.05. It performed that different values occur on the students' reading comprehension after the TBLT implementation. The significant value (p) also indicated that the output reaches 0.00 &lt; 0.05, which represented the result is under the significant level (0.05). It means that there is a significant influence of the TBLT method on the students' reading comprehension. In other words, TBLT method contributes positive in+uence on the students' English reading comprehension.