Reviewerly/exHarmony huggingface dataset:
Multi exposure image motion estimation based on improved optical flow

When capturing images, traditional imaging devices can only record images with low dynamic range (LDR) and cannot capture scene information with high dynamic range (HDR) in the real world due to their own response characteristics. Therefore, high dynamic range imaging technology has developed rapidly and gradually become a research hotspot in the field of digital image processing. The main problems of current multi-exposure images to generate HDR images include ghosting caused by camera shake and foreground objects moving, and information loss in overexposure and underexposure situations. To solve the ghost problem, the gradient direction histogram descriptor is used to process multi exposure images, and the brightness invariant motion estimation technology based on feature optical flow is used to generate high dynamic range images. The algorithm first selects the well exposed image as the reference image, and uses the gradient direction histogram descriptor which is robust to the illumination change to process the images with different exposure times, then uses the optical flow estimation algorithm to distort the input image to the reference image according to the corresponding relationship between image features ,finally synthesizes the final HDR image according to the weight map .The experimental results show that compared with the existing HDR algorithms, the proposed method has a certain improvement in performance.
Explainable AI For Data Farming Output Analysis: A Use Case for Knowledge Generation Through Black-Box Classifiers

Data farming combines large-scale simulation experiments with high performance computing and sophisticated big data analysis methods. The portfolio of analysis methods for those large amounts of simulation data still yields potential to further development, and new methods emerge frequently. Among the most interesting are methods of explainable artificial intelligence (XAI). Those methods enable the use of black-box-classifiers for data farming output analysis, which has been shown in a previous paper. In this paper, we apply the concept for XAI-based data farming analysis on a complex, real world case study to investigate the suitability of such concept in a real world application, and we also elaborate on which black-box classifiers are actually the most suitable for large-scale simulation data that accumulates in a data farming project.
Research and optimization of virtual studio’s lighting system with LED curtain wall

With the development of LED technology and the innovation of shooting concept, the virtualized production technology based on LED curtain wall has attracted much attention in film and television creation. This technology uses the virtual imaging of the LED curtain wall as the shooting background to achieve real-time rendering and production. At the same time, the LED curtain wall, which is composed of multiple pixels, also has the lighting function. This paper summarizes the current lighting technology in virtual filming based on LED curtain wall and proposes solutions for the LED curtain wall’s visible distance and Moire pattern during filming.
Introduction

With the development of the big data era, large-scale group decision making (LSGDM) has received more and more attention. In this chapter, we introduce the concept, characteristics and development status of LSGDM.
Optimization of registration link under infrared super resolution reconstruction

Image registration is a typical problem and technical difficulty in the research field of image processing, which can be used in infrared image fusion, image Mosaic, image segmentation, super-resolution reconstruction and other directions. On the basis of infrared image registration, considering the difference of infrared image, the effective accuracy of registration is set. The algorithm in this paper was initially used in super-resolution reconstruction, but it was redesigned in the registration process due to the need to reduce the computing cost in hardware implementation. The algorithm is improved on the basis of the original flownetS, and the cost of the calculate is reduced by 89% when the number of convolutions layers and the receptive field of the improved network are the same. There is no need for pooling layer, because pooling provides greater receptive field while reducing resolution, which will lead to spatial information loss and data loss. Dilate convolution convolution can avoid down-sampling and provide a larger sense field under the premise of the same amount of computation. Set different Dilation rates, receptive fields will be different, and there will be multi-scale information. This method based on deep learning uses the processing method of dilated convolution to greatly reduce the cost of network computing and provide a convenient channel for the hardware implementation of the algorithm. Moreover, the algorithm also achieves excellent results on the development board RK3399Pro. Combined with infrared image, this paper also demonstrates the difference between infrared image in the registration of effective accuracy and visible light, and also analyzes the target at different speed, the minimum network structure required. Finally, an appropriate ratio was selected through experimental attempts to ensure stable accuracy in the conventional moving target.
Visually capture quantum nonlocalities through quantum steering ellipsoid

Abstract Quantum steering ellipsoid (QSE) can faithfully characterize arbitrary bipartite state, and provide a new method to visually investigate and capture various quantum nonlocalities. Note that the dissipation of quantum nonlocalities can be accelerated by the coupling between the system and the decoherence channel, which induces great challenges in detecting quantum nonlocalities. Herein, considering the scenario in which the subsystem of a two-qubit system is coupled with a decoherence channel, the quantum discord (QD) and concurrence are visually characterized and detected by employing the QSE. The results reveal that the QSE y and z semiaxes are responsible for the traits of the QD and concurrence under the bit flip channel. The x and y semiaxes dominate and visualize the QD and concurrence under the phase damping channel. Of particular note is that one can realize the detections of the QD and concurrence via the shape of the QSE. To be clearer, the disappearance of the QD can be ascertained according to a needle-shaped QSE or a vanishing QSE in the Bloch sphere. In contrast, one can witness the concurrence if the total semiaxis length is greater than one. Beyond this, our results visually reveal that quantum entanglement is a stronger quantum nonlocality than the QD from the perspective of geometry, and thus deepens the understanding of the quantum nonlocality.
Employing Convolutional Neural Networks for Continual Learning

The main motivation for the presented research was to investigate the behavior of different convolutional neural network architectures in the analysis of non-stationary data streams. Learning a model on continuously incoming data is different from learning where a complete learning set is immediately available. However, streaming data is definitely closer to reality, as nowadays, most data needs to be analyzed as soon as it arrives (e.g., in the case of anti-fraud systems, cybersecurity, and analysis of images from on-board cameras and other sensors). Besides the vital aspect related to the limitations of computational and memory resources that the proposed algorithms must consider, one of the critical difficulties is the possibility of concept drift. This phenomenon means that the probabilistic characteristics of the considered task change, and this, in consequence, may lead to a significant decrease in classification accuracy. This paper pays special attention to models of convolutional neural networks based on probabilistic methods: Monte Carlo dropout and Bayesian convolutional neural networks. Of particular interest was the aspect related to the uncertainty of predictions returned by the model. Such a situation may occur mainly during the classification of drifting data streams. Under such conditions, the prediction system should be able to return information about the high uncertainty of predictions and the need to take action to update the model used. This paper aims to study the behavior of the network of the models mentioned above in the task of classification of non-stationary data streams and to determine the impact of the occurrence of a sudden drift on the accuracy and uncertainty of the predictions.
The Impact of Data Preprocessing on Prediction Effectiveness

This study considers a very important issue, which is the impact of preprocessing on model performance. On the example of data describing taxicab trips in New York City, a model predicting the average speed of a trip was built. The effectiveness of the obtained model was examined using relative error. The results were compared with the models obtained after prior data cleaning from the records containing missing data. Additionally, the effect of removing outliers on model quality was examined. An integral part of the paper is the description of a new method of anomaly detection. The author’s method involves fuzzy classification of the declared distance into three classes. As an indicator to allow for classification, the percentage of redundant distance with respect to Manhattan distance was selected. The results of a wide range of numerical experiments confirm the necessity of preprocessing. Comparison of a number of competing anomaly detection and prediction model building methods allows for reasonable generalization of the obtained conclusions. Additionally, the skillful use of fuzzy sets for anomaly detection allowed the development of a method that can be generalized to other transportation issues.
Automated Design of Dynamic Heuristic Set Selection for Cross-Domain Selection Hyper-Heuristics

Selection hyper-heuristics have been used successfully to solve hard optimization problems. These techniques choose a heuristic or a group of heuristics to create a solution and/or improve it. In a prior study, we proposed an approach that changes the heuristic set from which the hyper-heuristic is allowed to choose dynamically and that led to improving the performance of the hyper-heuristic. Previously, we manually designed the proposed approach which involved challenging design decisions and parameter tuning. In this study, we automate the design of the previously proposed approach using grammatical evolution to reduce human involvement in the design process. The proposed automated approach is evaluated on the domains of the CHeSC challenge. It is found that the automated design reduces the design time remarkably and performs as good as the manual design.
Diagnosis of the periodic vibration using laser interferometry

Temporal phase-shifting interferometry (TPSI) has been proposed for morphology monitoring of Plasma-Facing materials (PFMs) in Tokamak due to its high precision and far-field online capability. However, this monitoring method is sensitive to vibrational environment and the mechanical vibration in Tokamak, such as the Experimental Advanced Superconducting Tokamak (EAST), would lead to a decrease in the accuracy of the measurement or even wrong results. Real-time detection of the vibration parameters of the Tokamak during the plasma operation to compensate for the error of the TPSI measurement is a solution to improve the accuracy. In this paper, a laser interferometry platform for diagnosing and measuring vibration amplitude and frequency adopting the fringe-counting (FC) method has been introduced. We measured the different vibration amplitudes and frequencies using the proposed approach. The results show that the method has a potential application for in-situ measurement of surface morphology of PFMs in Tokamak like EAST.
Reproducibility of Facial Information in Three-Dimensional Reconstructed Head Images: An Exploratory Study

Facial information acquired via three-dimensional reconstruction of head computed tomography (CT) data may be considered personal information, which can be problematic for neuroimaging studies. However, no study has verified the relationship between slice thickness and face reproducibility. This study determined the relationship and match rate between image slice thickness and face detection accuracy of face-recognition software in facial reconstructed models.Head CT data of 60 cases comprising entire faces obtained under conditions of non-contrast and 1-mm slice thickness were resampled to obtain 2-10-mm slice-thickness data. Facial models, reconstructed by image thresholding, were acquired from the data. We performed face detection tests per slice thickness on the models and calculated the face detection rate. The reconstructed facial models created from 1-mm slice-thickness data and other slice thicknesses were used as training and test data, respectively. Match confidence scores were obtained via three programs, match rates were calculated per slice thickness, and generalized estimating equations were used to evaluate the match rate trend.In general, the face detection rates for the 1-10-mm slice thicknesses were 100, 100, 98.3, 98.3, 95.0, 91.7, 86.7, 78.3, 68.3, and 61.7 %, respectively. The match rates for the 2-10-mm slice thicknesses were 100, 98.3, 98.3, 95.0, 85.0, 71.7, 53.3, 28.3, and 16.7 %, respectively.The reconstructed models tended to have higher match rates as the slice thickness decreased. Thus, thin-slice head CT imaging data may increase the possibility of the information becoming personally identifiable health information.
Anonymity as a Shield

This chapter explains how anonymity is used as a shield. The blurry line between online activism and journalism sets the tone on how anonymous bloggers are empowered through the ability to hide part or all of their identities. The chapter covers the case of Dissent Doe, wherein anonymity had been crucial to maintaining her day job as a psychologist and engaging in risky speech and journalism that she believed would benefit society. It also discusses the case of MakeThemScared.com; an anonymous group that posted the names and sexual crimes of around seventy-five men amidst the MeToo movement. The commenters of MakeThemScared.com used their anonymity as a shield to protect them from the harmful consequences of speaking out.
Technological Protections for Anonymity

This chapter tackles technological protections for anonymity. While the First Amendment precedent has been essential in creating an atmosphere for online anonymity empowerment in the United States, understanding technology is needed to fully understand anonymity in the United States. The chapter highlights the concept of onion routing protecting anonymity, which involves the Tor browser. On the other hand, Virtual Private Network (VPN) offers services of encrypted tunnels through which people connect to the Internet. The chapter also explains how Tor has done so much benefit for dissidents, whistleblowers, marginalized communities and government intelligence operations. It discusses the correlation between Tor and the First Amendment in terms of legally protecting the anonymity of people.
In-Place Collaboration in Extended Reality Data Visualization

Throughout the past centuries technologies have evolved, and we are always looking for ways to make every persons' life easier. Analysing datasets consumes so much time, so interacting with real-life data visualisations is an innovative way to analyse any dataset. Augmented Reality is a new technology that have unlimited ideas, also collaborating over an augmented reality experience is adding a new way of implementing augmented reality to increase humans interaction with the data. This paper is about integrating a collaboration augmented reality experience over dataset visualisation. This paper scope includes the design, implementation and evaluation of the augmented reality experience. This AR experience includes 3 main modules which are Image tracking, data-set visualisation and collaboration. After implementing the AR experience surveys where taken from participants to measure experience usability and participants' task load.
QUANTUM MACHINE LEARNING

The article is devoted to the issues of quantum computing and algorithms aimed at implementing quantum machine learning both on separate quantum processors and in hybrid circuits using TPU and CPU.
Ethical norms of communication within the scientific community

The formation of the standards of the scientific community is considered – from the imperatives of R. Merton to the modern principles of scientific communication: the right to initiate scientific discourse; ensuring the reliability of published information; equality; focus on creating socially significant knowledge. Authors who violate ethical standards are subject to restrictions on participation in further scientific communication.
Why Study Crime News?

<bold>For many people, the mass media are the main source of their knowledge and perceptions about policing and crime, yet very little is known about the processes that shape the creation of crime news. This book aims to address this knowledge gap by examining the complex and contingent relationships over the last 40 years between the Metropolitan Police Service (MPS), the national news media and – more recently – the new investigative journalism start-ups. It seeks to re-evaluate classic criminological texts from the 1970s, 1980s and 1990s, suggesting that police/media/public relations might be even more complex than previously imagined and expanding the discussion to explore police/news media relations in the 2000s. It explores the impact of the Leveson and Filkin Reports and the advent of social media on police/media/public relationships; and it advances understanding of the role that digital start-ups are playing in expanding the remit of crime news.</bold>
An Overview of Artificial Intelligence Applications and Explosion

A general representation of the world as a computer program interprets its inputs. Some of the major traditional problems of philosophy arise in artificial intelligence Perhaps loss is a unique feature of hand-printed letters. We are at the threshold of becoming strongly connected and dominated by intelligent problem-solving machines. But our purpose is not to speculate on what the future holds. We should only attempt to describe and explain our first steps in building artificial intelligence are. This is another in a series of twenty-one articles on artificial intelligence published by Ellis. Harwood; RK, France, RK, Artificial Intelligence (XAI) project, the authors have done a good job of selecting many items. Understanding weaknesses and how they might react in future situation.
Hierarchical Model

We introduce the motivation of building a hierarchical model. For a dataset example, we implement different models, from a simple model to a hierarchical model, and discuss several features of hierarchical models. We also learn how to use hierarchical models with nonlinear models and how to handle data that include missing values.
Study of Augmented Reality for the Development of Learning at the Primary Education Level

Augmented reality is a technology that has appeared in recent years, having a significant impact on the lives of many people.
GAMIFICATION OF A PROFESSIONAL COURSE FOR MATH FOR SCHOOLCHILDREN WITH "MOODLE"

In this paper, career guidance and gamification tools are identified that contribute to the activation of cognitive activity, allow diversifying the types of educational activities and increase interest in the materials of a career guidance course in mathematics. As the subject content of building a career guidance course, the course "Theory of Probability" was chosen, which has interdisciplinary connections with any professional field. The possibilities of distance course gamification in LMS Moodle are analyzed. The constructed gamified course in mathematics in the Moodle system is aimed at solving the issues of career guidance and preparing students for the final exam in mathematics.
Implications and International Case Study on the Free emester(Year) System

본 연구는 “자유학기(년)제의 큰 틀은 무엇이고, 시도교육청 혹은 단위학교는 어디까지 자유학기(년)제 프로그램을 편성·운영할 수 있을 것인가?”에 대한 해답을 찾기 위해 자유학기(년)제와 유사한 정책을 펼치고 있는 국가들은 이 문제를 어떻게 해결하고 있는지를 살펴보고 그 시사점을 도출하는데 목적이 있다. 문헌분석 결과, 본 연구의 시사점은 다음과 같이 도출되었다. 첫째, 자유학기(년)제의 방향성을 수업 내용과 연계하여 좀 더 명확하게 규정할 필요가 있다. 둘째, 자유학기제 관련 활동과 교과외 활동인 창의적 체험활동 영역 간 중복을 탈피할 수 있는 방안이 고려되어야 한다. 또한 창의적 체험활동의 영역 및 시수를 조정하여 단위학교의 교육과정 편성·운영의 재량권을 확대시켜줄 필요가 있고, 자유학기(년)제 활동과 교과 활동과의 연계성 부족에 대한 해결책으로 교과내용과의 연계를 보다 분명히 규정해야 할 필요가 있다. 셋째, 자유학기제에서 연계학기, 혹은 자유학년제로 전면 확대할 때, 자유학기(년)제에서 규정하고 있는 지필고사의 미실시가 학기가 아닌 학년으로 반드시 이루어져야 하고, 이 때 학생들의 성장을 확인할 수 있는 제도적 장치를 마련할 필요가 있다. 이는 향후 고교학점제에서 개인의 교육과정을 설계하는데 기초자료로 활용되어야 할 것이다. 넷째, 자유학기(년)제가 중장기적으로 소기의 목적을 달성할 수 있도록 행․재정적 지원이 뒷받침 되어야 한다. 다섯째, 자유학기(년)제 관련 교사, 학생, 학부모, 지역사회 연수를 확대 운영할 필요가 있다.The purpose of this study is to examine how countries that are implementing policies similar to the free semester (year) system(FSS) are solving this problem and to draw implications. This study aims to find the answer to the question, “What is the framework of the FSS, and to what extent can provincial and provincial offices of education or unit schools organize and operate the FSS?” As a result of literature analysis, the implications of this study were derived as follows. First, it is necessary to define clearly the direction of the FSS in connection with the course content. Second, measures to avoid duplication between activities related to the FSS and creative experiential activities, which are extracurricular activities, should be considered. As a solution to the lack of connection between free semester (year) activities and subject activities, it is necessary to define more clearly the connection with subject content. Third, when the FSS is fully expanded from the linked semester or free year system, the non-execution of the paper-based test stipulated in the FSS must be done in the year, not the semester, and at this time, Institutional arrangements need to be established. Fourth, dministrative and financial support should be supported so that the FSS can achieve its intended purpose in the mid- to long-term. Fifth, it is necessary to expand and operate free semester (year) related training for teachers, students, parents, and local communities.
한국 성인역량 탐색과 요구분석

This study aims to identify competencies for Korea adults It also attempted to analyze the level of educational needs on essential adult competencies. Nineteen competencies were identified as core competencies for Korean adults to develop as a result of literature review and a Delphi method. Then, online survey on 19 adult competencies was conducted and data were collected from Korean adults with age range of 19 to 69. First, gap analysis was carried out to identify discrepancy between perceived importance of each competence and the current level of attainment of each competence using t-test and Borich needs assessment formula. Next, the Locus for Focus model was adopted to rank the competences, therefore to determine the highest priority of the competences. As a result, health-management competence, finance-management competence and self-development competence were identified as the highest-priority competencies for the Korean adults to develop. Based on the finding, the study presented practical implications for life-long education policy. The information on 19 competencies required for adults can be shared with lifelong education policy institutions and lifelong educational institutions to be used as basic data for developing competency-related policies and educational programs. It is also necessary to develop a scale for diagnosing the three highest-priority competencies, thus to understand the level of competency of each adult.