Reviewerly/exHarmony huggingface dataset:
A novel approach for weighted average filter and guided filter based on tunnel image enhancement

Image enhancement (IE) is a common thing we use to get better results from previous imagery. This image enhancement is not only used by us, but it is implemented in many fields. Such as implementation in the military field, medical field, legal field, industry field, entertainment field, and much more. The main use of IE in each field is to get clear information. Pedestrian detection is an essential way of support in current traffic management. Traditional pedestrian detection error &amp; miss detection rates are high owing to irregular lighting, dim tunnel atmosphere, and blurred controlled picture, making subsequent identifying hard. A rapid image enhancement (FIE) algorithm founded on picture model restriction is therefore suggested in this document and reduced to the pedestrian region of interest (ROI) in the pavement close the road under highway tunnel (HT) scene. First, the technique used to assess the local atmospheric light (LAL) by combining global atmospheric light (GAL) and partitioned atmospheric light (AL). Second, the transmission is predicted to be founded on the plan obtained as of the image model’s constraints. The third is for balancing tunnel illumination, the technique utilizes steady instead of illumination. Lastly, the picture of the tunnel is improved by the picture model. Moreover, we propose a narrowing region approach for improving the overall computing performance, due to the real-time requirements of the algorithm. Taking account of the highway tunnel features, which are a blurred scene and difficult to identify from the context, we use a multi-function integration approach to detect the enhanced image. We described a novel filter in this article that is commonly used in computer vision &amp; graphics. Guided algorithm filter is MATLAB simulated. Results of the experimental and comparative assessment indicate that the suggested technique can quickly and efficiently enhance the picture of the tunnel and highly enhance the impact of pedestrian detection.
Asian Corpus of English (ACE)

In recent years, the study of English as a lingua franca (ELF) has been gaining momentum. However, there have been few large-scale representative ELF corpora available for ELF research. The first such corpus, the Vienna-Oxford International Corpus of English (VOICE) capturing spoken ELF interactions in Europe, was successfully developed by a project team led by Prof. Barbara Seidlhofer (2005 to 2013). Inspired by VOICE, a project team led by Prof. Andy Kirkpatrick compiled the Asian Corpus of English (ACE), a one-million-word corpus capturing spoken ELF interactions in Asia, which was officially launched online (http://corpus.eduhk.hk/ace) in 2014. The corpus data have been tagged following the transcription conventions originally developed by the VOICE project team. These tags enable us to obtain a clear picture of the transcribed speeches and make ACE and VOICE comparable. In this chapter, details of the ACE corpus design from a corpus methodological perspective will be introduced, and different features of the ACE online platform will be illustrated. Research possibilities using ACE data will then be explored, and the theoretical and methodological concerns will also be discussed. It is hoped that ACE will fill a gap and open new windows in the area of ELF research.
Behavioural Analytics: A Preventative Means for the Future of Policing

Without sufficient intelligence, police response to crimes occurs in the form a reactive retort. This is even more so in the case of cyberspace policing, as digital platforms increase the complexities involved in the overall police incident response development. In this paper, we briefly introduce cybercrime and the necessities that police forces have to deal with. We argue that there is an urgent need for development and adoption of proactive and preventive techniques to identify and curb cyber and cyber-enabled crimes. We then present topic modelling as one of effective preventive techniques for predicting behaviours that can potentially be linked to cybercrime activities on social media.
Predicting Machine Errors based on Adaptive Sensor Data Drifts in a Real World Industrial Setup

We present a dynamic error prediction system for industrial production machines. We implemented a flexible data collection tool to create error warnings for a production line, which aims to improve the already existing static alarm models. For industrial machines, there are threshold-based alarm models set by prior experiences and observations of the operator. For machines without standardized interfaces and communication protocols, which are not Industry 4.0 compatible, it represents a challenge to implement and add a dynamic and opportunistic system behavior. Machines need to learn from past errors autonomously and adapt the production properties dynamically. We implemented a framework that makes production machines conform to the Internet of Things (IoT) concepts, by making previously non-IoT enabled resources available to get new insights into the production processes.The system component recognition and the database setup is done fully automatically by our developed system.We designed and applied a feature-based data drift model in a real-world industrial setting to determine data deviation between normal and erroneous work-pieces in real-time to predict upcoming erroneous behavior. The drift analysis flagged and predicted work-pieces as erroneous several minutes before the pre-defined machine alarms would have been raised. The resulting flagged sensors and values can be compared to the system determined errors to get new insights into the abnormal machine behavior. For the reduction of downtime, the most valuable immediate result of the system is the ability to notify the operator earlier and reduce overall downtime.
Making socio-legal Research More Social by Design: Anglo-German Roots, Rewards, and Risks

Abstract This Article looks for signs of Anglo-German life in the literature and practice under-pinning the current move to use “designerly ways” in socio-legal research; and asks whether design has a role to play in nurturing a sense of Anglo-German socio-legal community. It argues that a “sociological imagination” is essential if we are to fully understand possible synergies between design and socio-legal research, and the risks and rewards of activating them; and that while we cannot know what socio-legal research will or ought to look like in the coming months and years we must pay more attention to designing those moments that we are lucky enough to share in person.
Application of visible and long-wave infrared satellite data for hydrocarbon deposits prospecting

SUMMARY In the course of the work, Sentinel-2 satellite data were sequentially processed in order to statistically estimate the probability of detecting spectral anomalies of vegetation. These anomalies can be caused by the presence of hydrocarbon deposits. It was carried out on the basis of calculating the values of a number of spectral indices of vegetation for 20 pairs of polygons constructed within known hydrocarbon deposits and beyond it on a homogeneous area covered by vegetation. Similar work was carried out for 25 pairs of polygons for the results of calculating the surface temperature according to the data of a long-wave infrared survey obtained by the image processing of the Landsat-8 satellite TIRS sensor. The spatial data, sensitivity, and frequency of the Sentinel-2 and Landsat-8 satellite imagery data were found to be effective in solving the problem of detecting spectral and temperature anomalies associated with hydrocarbon deposits. This approach can be recommended as a means of additional information in the exploration of hydrocarbon deposits in areas with known proven deposits and as an auxiliary in the performance of geological prospecting for the identification of new deposits.
Physics Subject Headings (PhySH)

PhySH (Physics Subject Headings) was developed by the American Physical Society and first used in 2016 as a faceted hierarchical controlled vocabulary for physics, with some basic terms from related fields. It was developed mainly for the purpose of associating subjects with papers submitted to and published in the Physical Review family of journals. The scheme is organized at the top level with a two-dimensional classification, with one dimension (labeled “disciplines”) representing professional divisions within physics, and the other dimension (labeled “facets”) providing a conceptual partitioning of terms. PhySH was preceded in use by PACS (“Physics and Astronomy Classification Scheme”), which was in turn preceded by more ad hoc approaches, and this history and related vocabularies or categorizations will also be briefly discussed.
Redefining Visual Storytelling for Adaptation of Classic Literature in Immersive Environments: Hölderlin’s Echo VR

2020 celebrates the 250th birthday of the famous German poet Friedrich Hölderlin (1770–1843). The author Hannes Rall co-directs Hölderlin’s Echo VR, a serious game for the permanent exhibition in the Hölderlin Tower, in close collaboration with the Hölderlin Museum Tübingen. This paper will unpack the connection between the requirements of adaptation and the design choices that define visual storytelling in immersive space. As such, it aims to create a better understanding of best practice towards methods of adaptation design in VR.
A Novel Approach Towards Handwritten Digit Recognition Using Refraction Property of Light Rays

Due to the enormous application, handwritten digit recognition (HDR) has become an extremely important domain in optical character recognition (OCR)-related research. The predominant challenges faced in this domain include different photometric inconsistencies together with computational complexity. In this paper, the authors proposed a language invariant shape-based feature descriptor using the refraction property of light rays. It is to be noted that the proposed approach is novel as an adaptation of refraction property is completely new in this domain. The proposed method is assessed using five datasets of five different languages. Among the five datasets, four are offline (written Devanagari, Bangla, Arabic, and Telugu) and one is online (written in Assamese) handwritten digit datasets. The approach provides admirable outcomes for online digits whereas; it yields satisfactory results for offline handwritten digits. The method gives good result for both online and offline handwritten digits, which proves its robustness. It is also computationally less expensive compared to other state-of-the-art methods including deep learning-based models.
Modeling and Runtime Generation of Situation-Aware Adaptations

SitAdapt 2.0 is an architecture and runtime system for building adaptive interactive applications. The system is integrated into the PaMGIS framework for pattern- and model-based user interface construction and generation. This paper focuses on the situation-rule-based adaptation process and discusses the interplay of the SitAdapt 2.0 components in order to support different categories of adaptations. As the system observes the user during sessions and collects visual, bio-physical, and emotional data about the user that may vary over time, SitAdapt 2.0, in contrast to other adaptive system environments, is able to create situation-aware adaptations in real time that reflect the user’s changing physical, cognitive, and emotional state. The operation of the system is demonstrated with an example from an adaptive travel-booking application.
Study on Career Exploration of College of Education Students Who Do Not Want to Become a Teacher

이 연구는 교직을 희망하지 않은 사범대 학생을 대상으로 그들의 학교 적응과 진로 모색 과정을탐색하는 데 목적이 있다. 연구 결과, 첫째, 교직을 희망하지 않는 이유는 학교생활 자체에 부적응하거나, 교육 분야에 대한 지속적 관심은 있으면서 ‘교직’에 적성을 찾지 못한 경우가 포함된다. 둘째, 교직 이외의 진로 모색 과정에 대해서는 공무원 시험 준비와 같이 익숙한 것으로 대체하거나, 사범대수업을 통해 새로운 적성을 찾게 된 경우, 교생실습 또는 복수전공 선택 등 결정적 시기에 적성에 대해 다시 생각해본 경우가 있다. 셋째, 교직을 희망하지 않는 학생으로서 임용 대비 성격의 수업을 듣는 데 대한 회의감이 컸지만 다양한 교육적 이슈를 접할 수 있는 수업에서는 여전히 흥미를 가질 수있었고, 교원 임용 중심으로 이루어진 진로 지도에 대해서는 만족을 하지 못했다. 넷째, 교직을 희망하지 않는 학생들은 교원양성이라는 사범대 본연의 목적을 생각할 때 학교의 부가 역할에 대해 아예기대하지 않는다고 반응하면서도, 교직 이외의 진로 모색에서 사범대와 대학 차원의 적극성에 대한아쉬움을 표하였다. 이러한 연구 결과를 바탕으로 본 교직을 희망하지 않는 학생들의 특징은 첫째, 또 다른 “헤엄치거나 가라앉거나”(sink or swim), 둘째, 진로 선택에 대안 여전한 “이중성”으로 나타났다. 그리고 학생들은 교직의 “대체물 찾기”가 아닌 “새로운 발견”을 위한 방향으로 진로를 모색해야 하며, 교원양성기관이 이를 위한 지원자로서의 역할을 수행해야 할 것이다.This study was conducted to investigate the processes of career exploration of College of Education students who do not want to become a teacher. The results showed the followings: First, students don’t want to become a teacher because 1) they don’t adapt to the school life itself or 2) they don’t have “teaching aptitude” despite their consistent interests in the field of education. Second, students’ career exploration experiences other than teaching profession are 1) choosing a career path that is familiar as an alternative, such as studying for civil service exam, 2) discovering a new aptitude by taking courses in the College of Education, or 3) rethinking about teaching aptitude during critical career junctures for teaching practice or choosing a double major. Third, as students who do not want to become a teacher, they feel skeptical about taking the courses related to the Teacher Employment Test even though the students still have an interest in taking the courses in which they learn various issues in education. Fourth, students who do not want to become a teacher say they do not expect any help from school based on its primary purpose of the College of Education for teacher training; however, the students say it is regrettable that there is a lack of aggressive support from the College of Education and at the University level for career exploration other than teaching profession. Based on these results, the characteristics of students who do not want to become a teacher are shown to be as follows: first, they either sink or swim; second, they are “ambivalent” about the career. Students should search for career paths in the direction of “new discovery” rather than search for “career alternatives”, and the College of Education should play a role as supporters for this.
Preparing for Cyber Crisis Management Exercises

In this paper the authors discuss how to create a preparation schedule for exercises (PSE) to support EXCON-teams and instructors for full-scaled combined crisis management and cyber-exercises. The process to create the preparation schedule starts by performing vulnerability analysis to identify the most relevant and likely threats to the organization, before processing historical threats and attacks to further focus our simulation scenario development by planning and designing a socio-technical scenario. Moreover, a plan for simulation that are realistic and based on the organization’s maturity will be considered, and finally, in terms of a societal crisis impact exercise necessary lectures will be prepared. After this framework has been reviewed by the HCI International 2020, we plan to test the model when planning for exercises at the Norwegian Cyber Range (NCR) environment. NCR will be an arena where testing, training, and exercise will be used to expose individuals, public and private organizations, government agencies to simulate socio-technical cyber security events and situations in a realistic but safe environment.
Riemann Hypothesis from the Physicist’s Point of View

This article presents an attempt to comprehend the evolution of the ideas underlying the physical approach to the proof of one of the problems of the century - the Riemann hypothesis regarding the location of non-trivial zeros of the Riemann zeta function. Various formulations of this hypothesis are presented, which make it possible to clarify its connection with the distribution of primes in the set of natural numbers. A brief overview of the main directions of this approach is given. The probable cause of their failures is indicated - the solution of the problem within the framework of the classical Turing paradigm. A successful proof of the Riemann hypothesis based on the use of a relativistic computation model that allows one to overcome the Turing barrier is presented. This model has been previously applied to solve another problem not computable on the classical Turing machine - the calculation of the sums of divergent series for the Riemann zeta function of the real argument. The possibility of using relativistic computing for the development of artificial intelligence systems is noted.
Using Flask for SQLIA Detection and Protection

At present the web applications are used for most of the life activities, these applications are affected by an attack called (Structure Query Language Injection Attack) SQLIA due to the vulnerabilities of the web application. The vulnerabilities of the web application are increased because most of application developers do not care to security in designing.SQL injection is a common attack that infects a web application. The attacker adds (Structured Query Language) SQL code to web page for accessing and changing victim databases.The vital step in securing the database and detecting such an attack in web apps is preparing a tool. Many researchers propose different ways for detection and prevention of such as an attack. In this paper a tool it proposed using a powerful micro-framework web application designer called Flask in Python 3.7 to detect and prevent such attacks. The proposed system is called SQLIAD. SQLIAD analyzed a web application on-line.
Social Network Analysis

Sentiment analysis has become an important field in scientific research in recent years. The goal is to extract opinions and sentiments from written text using artificial intelligence algorithms. In this article, we propose a new approach for classifying Twitter data into classes (positive, negative, and neutral). The proposed method is based on two approaches, a dictionary-based approach using the sentimental dictionary SentiWordNet, and an approach based on the fuzzy logic system (fuzzification, rule inference, and defuzzification). Experimental results show that our approach outperforms some other approaches in the literature and that by using the fuzzy logic we improve the quality of the classification.
MOBILE AUGMENTED REALITY FOR BIOLOGY LEARNING: REVIEW AND DESIGN RECOMMENDATIONS

In the last decade, the implementation of mobile Augmented Reality (AR) has transformed the conventional biology classroom environment. However, only limited articles were found to review the learning content of AR applications for learning biology. Thus, this paper presents a review and analysis of the development of mobile AR applications for learning biology. Four articles that reported the development of AR learning apps for biology from indexing databases were analyzed (Scopus = 3, ScienceDirect = 1). In addition, three apps for learning Biology from Google Play were reviewed and recommended. For the implication, we recommend five important features for developing science AR apps. This synthesized review would benefit teachers and educators, thereby suggesting a path for future work.
Prediction of Indonesian Inflation Rate Using Regression Model Based on Genetic Algorithms

Inflation occurs where there is an increase in the price of goods or services in general and continuously in a country. Uncontrolled inflation will have an impact on the decline of the Indonesian economy. Therefore, the prediction of future inflation levels is necessary for the government to develop economic policies in the future. Prediction of inflation levels can be done by studying historical past Consumer Price Index (CPI) data. Regression methods are often used to solve prediction problems. The problem of finding the optimal prediction model can be seen as an optimization problem. Genetic algorithms are often used to deal with optimization problems. Thus, this work proposed to use a genetic algorithm-based regression model for predicting inflation levels. The model was trained and evaluated using real CPI data which obtained from the Indonesian Central Bank. Based on the experiment, it is proved that the proposed model is effective in predicting the inflation level as it gains MSE of 0.1099.
Research progress on the soil vapor extraction

Soil vapor extraction (SVE), the most common, efficient and economical means of remediation, is an in-situ remediation technique for removing volatile pollutants from unsaturated soil. The paper briefly introduced the technological rationale and cha?racteristics, summarized the theories and application research for SVE at home and abroad, and made the expectations and suggestions for the research on SVE. The international scholars have systematically researched the influence factors, remediation mechanism and numerical simulation of SVE. At present, SVE has been mostly integrated with other techniques to form enhanced SVE techniques, such as thermally enhanced SVE and AS-SVE (Air sparging-SVE), to be used for the field remediation widely. Compared with foreign countries, researches of Chinese scholars mainly focus on the laboratory research, especially on the influence factors, but rarely study the SVE model and the mass transfer mechanism of pollutant in SVE process. The SVE pilot studies are rare in China, and the field application has not been reported. In view of this situation, Chinese scholars in the future research can focus on the following aspects: (1) strengthening the research and systematized summary of SVE technical parameters and related knowledge; (2) strengthening the research on the mechanism and model of gas-phase mass transfer of pollutants in soil during SVE process; (3) strengthening the research on the enhanced SVE techniques and its application to actual site remediation.
A Study on Creation a Learning Dataset for the Development of an Unmanned Power Facility Monitoring System based on Deep Learning

Since the emergence of deep learning technology, research and development using it in various industries has been in progress. In the power field, deep learning technology based on image recognition has been in the spotlight for the development of condition diagnosis and monitoring systems. However, in order to increase the performance of the deep learning model, it is necessary to build a large data set for learning it as well as the deep learning model. Therefore, in this paper, we propose a dataset creation method for development of unmanned power facility monitoring system based on deep learning. In an environment in which a PTZ camera and a 360-degree camera are operated simultaneously, various method for generating learning data were proposed, and the performance of the learning data set was proved through the detection system experiment.
Temperature-based inputs for molecular reservoir computers

We design and implement a temperature-based input mechanism for molecular reservoir computing. Using temperature allows us to interact with the system while keeping it chemically closed, a crucial ...
Whoever Controls the Media, Controls the VUI

Older adults (OAs) form the fastest growing user group of voice user interfaces (VUIs). VUIs hold promise for OAs in terms of usability and convenience. However, OAs' perceptions of VUIs can be influenced by the messages about and portrayals of technology they receive from mass media. This in turn can impact their adoption and the extent of the benefit they can derive from VUIs. In this paper, we present a preliminary analysis of the mass media articles published by AARP, a leading organization aimed at empowering adults aged 50 and up, on the topic of VUI. We use inductive thematic analysis to find the themes of the selected articles. Through this, we aim to gain a better understanding of VUIs' perceived value and utility for OAs as portrayed in media, and the implications of this portrayal on the possible strategies for increasing VUI adoption by OAs.