Reviewerly/exHarmony huggingface dataset:
FINANCIAL REPORTING STANDARDS ADOPTION AND FOREIGN DIRECT INVESTMENT IN NIGERIA AS MODERATED BY INSTITUTIONAL QUALITY

The adoption of IFRS enhanced the transparency of stewardship reporting and thus improved the investment ability of countries affected. The study examined the moderating effect of institutional quality on the relationship between international financial reporting standards and foreign direct investment in Nigeria from 2012 to 2018 Institutional quality was measured by political stability and control of corruption while foreign direct investment was measured by foreign investment on equity, foreign portfolio investment on money market and foreign direct investment on trade credits. Ex-post facto research design was adopted and the Generalised Methods of Moments (GMM) was used for the analysis. The study found that with the aid of institutional quality, the IFRS has a significant effect on investment inflows in Nigeria. The study concludes that international financial reporting standards has a significant influence on foreign direct investment with strengthened institution and anticorruption efforts in Nigeria. Therefore, the study recommends that the Nigerian government should strengthen its institutional mechanisms to fully benefit from the adoption of IFRS and drive inflows of foreign direct investment.
Significance of educational modeling during the COVID-19 coronavirus pandemic

The article shows computer simulation of the mechanical, thermal power systems and electronics and automation systems for the modern educational process organized remotely during the COVID-19 coronavirus pandemic. The article describes the computer models made by the author, analyzes and highlights the positive aspects of such simulation for conducting distant learning experiments, visual and detailed presentation of theoretical material and making conditions for obtaining high-quality education even under difficult pandemic conditions.
Strings

In this chapter, you will learn: what is a String object, how to create them, how to manipulate them, how to use Strings in switch statements or expressions, how to use StringBuilder and StringBuffer, and how to create multiline strings.
Books and Book Chapters

This chapter addresses the process of proposing authored books, edited books, and handbooks, as well as assembling the book itself. The chapter also covers book chapters and whether scholars should write a chapter that they have been invited to write. The challenges in putting together an authored book are outlined where the challenges are parallel to those involved in writing non-empirical papers. Edited books are discussed in terms of identifying content to be covered, choosing and inviting chapter authors, accounting for chapters that will not materialize, and working with authors to revise and finalize their chapters. Edited handbooks are reviewed as large projects that can impact fields of study but that require a great deal of work. Readers considering whether to accept book chapter invitations are advised to consider what would be written in the chapter and to acknowledge that the chapter would then represent the source material for those ideas.
Keynote 1: A Conversation with Dr. Safiya Noble

Summary form only given, as follows. The complete presentation was not made available for publication as part of the conference proceedings. This keynote will provide audience members with an opportunity to discuss topics of interest with noted digital media and information sciences scholar, Dr. Safiya Noble.
Development of a Faster R-CNN-based Marine Debris Detection Model for an Embedded System

In this study, we propose a Faster R-CNN-based marine debris detection algorithm for an embedded system. First, the trash annotations in context (TACO) dataset, which is an open image dataset of waste in the wild, is used to build a training image dataset of marine debris. To enhance the amount of our training dataset, we included images of the TACO unofficial dataset, which has not been reviewed by the TACO research team. To this end, we manually screened the appropriately annotated images from the TACO unofficial dataset. In addition, only seven most frequently discovered classes in the ocean are selected from the TACO datasets to enable efficient learning. The utilization of MobileNet as the backbone network of the proposed Faster R-CNN model enables a faster inference time compared to those of conventional models. In addition, the backbone network was fine-tuned on the TACO dataset to improve the feature extraction performance of the model. Lastly, the real-time operability of the proposed algorithm was verified by porting the model to Jetson Xavier NX.
A Grey Wolf Optimizer Feature Selection method and its Effect on the Performance of Document Classification Problem

Optimization methods are considered as one of the highly developed areas in Artificial Intelligence (AI). The success of the Particle Swarm Optimization (PSO) and Genetic Algorithms (GA) has encouraged researchers to develop other methods that can obtain better performance outcomes and to be more responding to the modern needs. The Grey Wolf Optimization (GWO), and the Krill Herd (KH) are some of those methods that showed a great success in different applications in the last few years. In this paper, we propose a comparative study of using different optimization methods including KH and GWO in order to solve the problem of document feature selection for the classification problem. These methods are used to model the feature selection problem as a typical optimization method. Due to the complexity and the non-linearity of this kind of problems, it becomes necessary to use some advanced techniques to make the judgement of which features subset that is optimal to enhance the performance of classification of text documents. The test results showed the superiority of GWO over the other counterparts using the specified evaluation measures.
Feature Explanation Algorithms for Outliers

In this paper, we study feature explanation and propose two algorithms to find a subset of features that are responsible for making the dataset anomalous. The first algorithm employs isolation forest and binary particle swarm optimization. The second algorithm is based on a greedy approach, which also uses an isolation forest. We experiment and compare the proposed algorithms on four synthetic datasets, air quality data from the UCI machine learning repository, and eight time stamps of air quality data. We find that the isolation forest incorporated with binary particle swarm optimization outperforms the greedy approach on stability and the proposed performance metric called feature consistency.
A novel approach for Software Architecture Product Line Engineering

A large software system exists in different forms, as different variants targeting different business needs and users. This kind of systems is provided as a set of “independent” products and not as a “single-whole”. Developers use ad-hoc mechanisms to manage variability. We defend a vision of software development where we consider an SPL architecture starting from which the architecture of each variant can be derived before its implementation. Indeed, each derived variant can have its own life. In this paper, we propose a novel approach for Software Architecture Product Line (SAPL) Engineering. It consists of, i) a generic process for recovering an SAPL model which is a product line of “software architectures” from large-sized variants. ii) a forward-engineering process that uses the recovered SAPL to derive new customized software architecture variants. The approach is firstly experimented on thirteen Eclipse variants to create a new SAPL. Then, an intensive evaluation is conducted using an existing benchmark which is also based on Eclipse IDE. Our results showed that we can accurately reconstruct such an SAPL and derive effectively pertinent variants. Our study provides insights that recovering SAPL and then deriving software architectures offers good documentation to understand the software before changing it.
Bad Tools Hurt: Lessons for teaching computer security skills to undergraduates

Understanding why developers continue to misuse security tools is critical to designing safer software, yet the underlying reasons developers fail to write secure code are not well understood. In order to better understand how to teach these skills, we conducted two comparatively large-scale usability studies with undergraduate CS students to assess factors that affect success rates in securing web applications against cross-site request forgery (CSRF) attacks. First, we examined the impact of providing students with example code and/or a testing tool. Next, we examined the impact of working in pairs. We found that access to relevant secure code samples gave significant benefit to security outcomes. However, access to the tool alone had no significant effect on security outcomes, and surprisingly, the same held true for the tool and example code combined. These results confirm the importance of quality example code and demonstrate the potential danger of using security tools in the classroom that have not been validated for usability. No individual differences predicted one’s ability to complete the task. We also found that working in pairs had a significant positive effect on security outcomes. These results provide useful directions for teaching computer security programming skills to undergraduate students.
Evolution of XR Research in Brazil according to the first 22 SVR editions

Although Virtual Reality technology was first developed almost sixty years ago, there has been little survey work giving an overview on how research in VR, AR and MR evolved in Brazil along with their future trends. We provide such analysis by reviewing the development made since the first WRV event, back in 1997, until SVR 2020. The first 22 event editions may help understand how the area was explored and provide a roadmap for future research. The 609 full papers analyzed were compiled into an open catalog, available on the internet. It features important filter capabilities, enabling the quick selection of papers based on citation number, conference topic, area of application, user experiments, statistical analysis and so on. We hope this tool to be of great value to the field, and also for helping researchers decide which topics should be explored when they are beginning their own studies in the area. In this analysis, we also refer to the most frequent authors in the area and how they contributed to the field based on their expertise and research group.
Handwritten Documents Validation using Pattern Recognition and Transfer Learning

Handwritten documents in an Enterprise Resource Planning (ERP) system can come from different sources and usually have different designs, sizes, and subjects (i.e. bills, checks, invoices, etc.). Given these documents were filled manually, they have to be inspected to detect various kinds of issues (missing signature or stamp, missing name, etc.) before being saved in the ERP system or processed by an OCR engine. In this paper, the authors present a transfer learning approach to detect issues in scanned handwritten documents, using an award-winning deep convolutional neural network (InceptionV3) and different machine learning algorithms such as Logistic Regression (LR), Support Vector Machine (SVM) and Naive Bayes (NB). The experiment shows that the combination of InceptionV3 and LR got an accuracy of 91.8% for missing stamp detection. This can allow using this approach in an ERP system as an automatic verification procedure in a document processing flow.
New Explainability Method based on the Classification of Useful Regions in an Image

Machine learning is a necessary and widely used tool nowadays in industry. Talking about the evaluation of its reliability, already known metrics are broadly used, but they are focused on how precise, accurate or sensitive the model is. Nevertheless, these metrics do not offer an overview of the consistency or stability of the predictions, that is, how much reliable the model is, which could be deduced if the reasons behind the predictions are understood. In the present work, we propose a novel method that can be applied to image classifiers and allows the understanding, in a non-subjective visual manner, of the background of a prediction.
A Research for the Competencies of Dance Educator in the New Normal Era

The purpose of this study was to investigate dance educator’ core competencies. For this study, we first reviewed other research literatures on dance teachers'' expertise and core competencies. And explored the extracted major competencies related educator’ roles, based on our analysis of the interview results, acquired from educators in the dance education area.
A Survey of the State of Research on Augmented Reality from a Business Perspective using Porter’s Value Chain

In recent years, augmented reality (AR) technology has been able to demonstrate more and more impressively the potential it brings for companies and their valueadding activities, and this even though acceptance of the technology in society is only just beginning. Due to this, our work aims to bring a comprehensive overview of AR deployment opportunities based on the value chain, forcing a symbiosis of potential demonstration and acceptance promotion. For our investigation, we consider the most important peer-reviewed papers on the state of research on augmented reality from a business perspective and provide a comprehensive overview of the different possible uses of AR within a company, structured according to Porter’s value chain, as well as an outlook on future research on the expansion and further development of AR systems. Based on this, we formulate research gaps for future work on AR in the context presented.
79 Statistical Graphics and Interactive Visualization in Animal Science

Abstract Statistical graphics has advanced significantly in recent years with the development of statistical computing tools that allow us to create reports dynamically, facilitate reproducible research, and explore data interactively. In particular, data visualization is a fundamental aspect of big data analysis in animal science. However, the static nature of standard visualization limits the information that can be displayed and extracted. The objectives of this hands-on workshop are to learn how to utilize interactive visualization and investigate both global and local structures of graphs with useful zooming in and zooming out capabilities. We will use the Shiny R package, which is a web application framework for R. A Shiny application has great potential to deliver interactive data analysis and visualization in a web browser. Yet there is limited application of this type of tool in agricultural science. We will learn the capabilities of R Shiny and its use with example applications in animal science and how to aid scientific discoveries and decision-making processes using interactive data exploration tools. After taking this workshop, the participants will be able to understand the concept of R Shiny and develop a web-based interactive visualization tool. The interactive and integrative data visualization features embedded in Shiny applications offer a new resource for users to readily extract extensive information from complex data.
Discovering Video Integrity through Blockchains

Video surveillance has been increasing in popularity in the recent years. This is due to the advancements in the sensor technology that has led to increased affordability of cameras and other video capturing approaches. This has led to a large number of organizations and individuals utilizing the video surveillance to deter criminals from committing the crime. This evidence can be utilized in court to facilitate the justice department effectively for investigation. As there have also been improvements in the approaches to tamper and edit the video footage effectively, there is a need to achieve video integrity to effectively prove the authenticity of the footage. For this purpose, this research article has been effective in achieving video integrity evaluation through the utilization of the distributed Blockchain framework. The presented technique utilizes Advanced Encryption System along with Blockchain framework to achieve effective and useful video integrity evaluation. The experimental outcome has been effective in demonstrating the superiority of the proposed approach.
Low-Cost Camera-Based Smart Surveillance System for Detecting, Recognizing, and Tracking Masked Human Face

A surveillance system is still the most exciting and practical security system to prevent crime effectively. The primary purpose of this system is to recognize the identity of the face caught by the camera. With the advancement of the Internet of things, surveillance systems were implemented on edge devices such as the low-cost Raspberry mobile camera. It raises the challenge of unstructured image/video where the video contains low quality, blur, and variations of human poses. The challenge is increasing because people used to wear a mask during the Covid -19 pandemic. Therefore, we proposed developing an all-in-one surveillance system with face detection, recognition, and face tracking capabilities. This system integrated three modules: MTCNN face detector, VGGFace2 face recognition, and Discriminative Single-Shot Segmentation (D3S) tracker to create a system capable of tracking the faces of people caught on surveillance camera. We also train new face mask data to recognize and track. This system obtains data from the Raspberry Pi camera and processes images on the cloud as a mobile sensor approach. The proposed system successfully implemented and obtained competitive results in detection, recognition, and tracking under an unconstrained surveillance camera.
Ensemble based on Accuracy and Diversity Weighting for Evolving Data Streams

Ensemble classification is an actively researched paradigm that has received much attention due to increasing real-world applications. The crucial issue of ensemble learning is to construct a pool of base classifiers with accuracy and diversity. In this paper, unlike conventional data-streams oriented ensemble methods, we propose a novel Measure via both Accuracy and Diversity (MAD) instead of one of them to supervise ensemble learning. Based on MAD, a novel online ensemble method called Accuracy and Diversity weighted Ensemble (ADE) effectively handles concept drift in data streams. ADE mainly uses the following three steps to construct a concept-drift oriented ensemble: for the current data window, 1) a new base classifier is constructed based on the current concept when drift detect, 2) MAD is used to measure the performance of ensemble members, and 3) a newly built classifier replaces the worst base classifier. If the newly constructed classifier is the worst one, the replacement has not occurred. Comparing with the state-of-art algorithms, ADE exceeds the current best-related algorithm by 2.38% in average classification accuracy. Experimental results show that the proposed method can effectively adapt to different types of drifts.
Anomaly Detection for Wide-Field Images Using Gaze Prediction Module

In this paper, we tackle anomaly detection in cluttered wide-field images. Typically, the conventional approaches perform patch-wise anomaly detection by cropping the original image. However, it is difficult to detect anomalies correctly when the image has significant variations and local anomalous areas. Therefore, inspired by the human visual inspection, we propose a novel anomaly detection framework called Gaze-based Anomaly Detection (GAD). Our GAD learns a gaze map obtained from inspectors and utilizes the map to pay attention to the anomalous areas. The experiment showed that the proposed method allows us to detect abnormal samples without cropping and localization pre-processing and outperforms the conventional ones.
Ultra-HD Video Streaming in 5G Fixed Wireless Access Bottlenecks

5G Fixed Wireless Access (FWA) is an enabling technology in intelligent systems (IS) that may provide Ultra-HD (UHD) video streaming services with high Quality of Experience (QoE) in a small business use case setting. However, UHD streaming over 5G FWA is difficult in terms of latency and dependability due to numerous network factors. Due to this there may be multiple video players competing for network resources when streaming a UHD video. To date there has been very little work of 5G ‘last mile access’ streaming over bottleneck FWA Local Area Networks (LANs) under congested network conditions. The bottleneck link is the 5G FWA gateway. In these networks viewers may get sub-optimal QoE. Adaptive bitrate (ABR) algorithms are used to select the near optimal bitrates during a streaming session. To obtain the QoE of viewers in 5G FWA bottleneck networks we study the performance of four DASH-based adaptive video streaming algorithms (MPC, BOLA, Oboe and Pensive). BOLA performs the best and Pensive the worst. However, BOLA’s overall performance is sub-optimal. This work supports the need for developing new ABR algorithms for the 5G FWA environment.
Scaremongering, threats and misinformation

British Journal of Child HealthVol. 2, No. 5 EditorialScaremongering, threats and misinformationCaroline VoogdCaroline VoogdEditorSearch for more papers by this authorCaroline VoogdPublished Online:9 Dec 2021https://doi.org/10.12968/chhe.2021.2.5.213AboutSectionsView articleView Full TextPDF/EPUB ToolsAdd to favoritesDownload CitationsTrack CitationsPermissions ShareShare onFacebookTwitterLinked InEmail View article References Joint Committee for Vaccination and Immunisation. JCVI statement on COVID-19 vaccination of children aged 12 to 15 years: 3 September 2021. 2021. https://bit.ly/3BxTp6H (accessed 11 October 2021) Google ScholarMedicines and Healthcare products Regulatory Agency. Coronavirus (COVID-19) vaccine adverse reactions. 2021. https://www.gov.uk/government/publications/coronavirus-covid-19-vaccine-adverse-reactions (accessed 11 October 2021) Google ScholarSecEd. Anti-vax protests: Warning over fake NHS consent form. 2021. https://bit.ly/3oTc5ua (accessed 11 October 2021) Google Scholar FiguresReferencesRelatedDetails 2 October 2021Volume 2Issue 5ISSN (print): 2633-5417ISSN (online): 2633-5425 Metrics History Published online 9 December 2021 Published in print 2 October 2021 Information© MA Healthcare LimitedPDF download