Reviewerly/exHarmony huggingface dataset:
Exploring Blockchain-based Government Services in India

Today, the world mainly relies on information that is generated and distributed among the public on a large scale, but unfortunately, the authenticity of the information is always in question. Recently, a new technological breakthrough called blockchain has redefined the enterprise of “trust”, making the information business inherently trustworthy. This chapter will focus on the ongoing exploration of blockchain, a distributed ledger technology (DLT) that is considered disruptive in the industry due to its unique transparent and tamper-proof features and how it has opened up new opportunities in various sectors, including government services, whose application can provide operational efficiency and transparency in the system and create value for citizens. Similarly, with the advent of Digital India Initiatives, the government of India is moving towards being a digitally empowered nation, with technology being the prime focus. As blockchain is one of the emerging technologies in the industry, various pilot exercises are being done in an Indian context in many states. The initial part of the chapter will focus on understanding the application of blockchain in government services to gain efficiency and increase transparency and accountability in their functioning, and its implementations in government services, like the development of government certificates, land registry, creating tamper-proof voting records, vehicle registries, etc. The next part of the chapter will focus on the global adoption of blockchain, since various countries are in the league of experimenting with this technology in the public sector. One effective case of blockchain adoption and implementation in the country of Estonia will be discussed in depth, as its adoption of blockchain technology can be seen as a successful programme plan for potential blockchain applications elsewhere. Lastly, the chapter explores institutional initiatives like the Centre of Excellence (CoE) for blockchain technology, set up by Karnataka to promote blockchain and its application; regulatory approaches like the draft national strategy on blockchain, which was submitted recently by the National Institute for Smart Governance (NISG) to provide a set of policy frameworks to proactively facilitate the integration of blockchain with existing economic ecosystems; and a blockchain strategy paper by the government of India’s policy think tank NITI Aayog.
An Analytical Performance Evaluation of MapReduce Model Using Transient Queuing Model

Today the MapReduce frameworks become the standard distributed computing mechanisms to store, process, analyze, query and transform the Bigdata. While processing the Bigdata, evaluating the performance of the MapReduce framework is essential, to understand the process dependencies and to tune the hyper-parameters. Unfortunately, the scope of the MapReduce framework in-built functions is limited to evaluate the performance till some extent. A reliable analytical performance model is required in this area to evaluate the performance of the MapReduce frameworks. The main objective of this paper is to investigate the performance effect of the MapReduce computing models under various configurations. To accomplish this job, we proposed an analytical transient queuing model, which evaluates the MapReduce model performance for different job arrival rates at mappers and various job completion times of mappers as well as the reducers too. In our transient queuing model, we appointed an efficient multi-server queuing model M/M/C for optimal waiting queue management. To conduct the experiments on proposed analytics model, we selected the Bigdata applications with three mappers and two reducers, under various configurations. As part of the experiments, the transient differential equations, average queue lengths, mappers blocking probability, shuffle waiting probabilities and transient states are evaluated. MATLAB based numerical simulations presented the analytical results for various combinations of the input parameters like λ, µ1 and µ2 and their effect on queue length.
Electronic Invoicing Using Image Processing and NLP

In the world of e-commerce, recording sales play a significant role. To achieve it, companies use invoices, and each seller generates an invoice on each transaction. As tons of invoices are generated, by different sellers on a particular day, in their formats, it is challenging for the hosting company to keep track of the trades and transactions. They have to salvage information from each invoice manually as they are in different templates. We found out that almost all decent performing products in this area operate on standard pdf, which consists of various elements enclosing the information, and each of these elements can be selected to extract information. However, these solutions do not perform well on scanned pdf which has no such containers. All the information in this pdf is in one single container as an image element. This type of invoices forms a major portion of the total invoice generated. The products which do support scanned pdf data extraction are expensive. We propose an innovative, cost-effective, and extensive solution to this problem. It will automate the invoicing process and convert all the invoices to a standard structured template, which is predefined by the user. We use cutting edge algorithms, which can detect information in the given invoice easily. They detect the information required following a tag-based system, which is programmed internally. After successful detection of the region of interest (ROI) by localizing bounding boxes around them, the data are extracted from each bounding box using OCR techniques and stored in various data structures. The obtained data generate an Excel file in the defined template automatically. A user-friendly GUI allows annotation of new invoice formats and prepares the dataset for the future. The solution helps the company to redirect its resources currently deployed in it, to other areas, which is beneficial for the company.
Breast Cancer Melanoma Prediction using Two Layer Deep Neural Network

Breast cancer is one of most commonly diagnosed cancer in the world and second most cause of death after lung cancer.The USA cancer society estimate 284200 women will be diagnosed with BC and 44130 will die due to this disease in 2021.The symptoms of breast cancer include a lump in the breast, bloody discharge from the nipple, chronic pain and changes in the shape or texture of the nipple or breast.BC cancer is classified in two class i. Benign ii.Malign through ML techniques to observe the risk of disease for patient.In this time, Machine Learning (ML) techniques are preferred to classify of BC to achieve best efficiency in diagnoses of BC.In this paper, author wants classification of Breast cancer by using the two layers Deep Neural Network with applying two method gradient & back propagation.Neural Network model generates better result 99.45% as compare to NB classifier 96.19% and KNN classifier 97.51% with minimum error.
Quantum Electromagnetics Technology [Special Series: Guest Editorial]

The article “Quantum Radars and Lidars: Concepts, Realizations, and Perspectives,” by Gregory Slepyan, Svetlana Vlasenko, Dmitri Mogilevtsev, and Amir Boag is one that stretches the imaginations of conventional practitioners of radar technology. The quantum advantage that quantum sensing will deliver is too great to be ignored, even though naysayers may not agree on this path. Also, the knowledge base in quantum technologies is incredibly thin, and, hopefully, this article will spur more engineers and scientists to take up the challenge of working in this area, sharing their experience and knowhow as well as expanding the knowledge base. We have seen the recent exciting growth in quantum computing technology, and we wish for the same excitement in quantum sensing technology. The ability to use entangled photon pairs to enhance the detection of weak photons will be beneficial to other applications, such as quantum sensing and quantum imaging in biomedical engineering and health care. Entangled photon pairs are also useful in quantum communication.
Analyzing entrepreneurship-growth nexus across high and low income countries

The emergence of large industries (the industrialization era) were considered the key drivers of economic growth in the mid-18th century through to the early part of the 19th century (Bruns, 2011). During this period, most of these large scale industries enjoyed economies of scale and this made them more efficient. Due to this, most economies, particularly the developed ones paid so much attention to the growth and expansion of large firms whiles micro, small and medium scale enterprises as well as entrepreneurship were thought out to impede economic growth and development. However, in recent years, entrepreneurship has become a central issue and as such the focus and attention has been shifted from large scale manufacturing and industrialization towards entrepreneurship. This could be attributed to advancement in the service sector, technological change, the quest for people to have their own businesses, to mention a few. This paper therefore examines the role entrepreneurship plays in economic growth and development across some selected high and low income countries. A longitudinal estimation technique is adopted to make the comparison. This approach concurrently account for the dynamic effect of the entrepreneurship and economic growth nexus across the cluster of countries. Data on 39 high income countries as well as 24 low income countries from a period of 1999 to 2019 were considered. It was observed that entrepreneurship has a positive impact on growth across the selected high income group of countries but within the context of low income group of countries, entrepreneurship does not necessarily aid growth.
Adaptation for Vigenère Cipher Method for Auto Binary Files Ciphering

Protecting the private information from third party became an important issue in computer science nowadays; cryptography is a science of encryption and decryption information. The first use of cryptography was in ciphering text messages, with the developing of computer usage; other type of files has been appeared such as: picture, audio, video, etc. These types of files can carry private information that needs to be saved from the third part. This paper based on design and implement system for auto encryption/decryption binary files by adapting well-knownVigenère encryption methods, by converting binary files to stream of bytes and encrypting each byte of this stream. The result shows the complete auto protection for the binary files.
Hear - Taste - See: UISGE BEATHA - Waters of Life

A special kind of synaesthetic experience could be seen, heard and tasted at the lecture series back in 2019/2020 about the format »Works and Whiskies«, as very different tastes of Scotch whisky flavours were transformed into notes and pieces of music for contrabass clarinet by composer Petra Stump-Linshalm. These were rendered audible by musician Heinz-Peter Linshalm and found their visual equivalent in paintings by Jutta Goldgruber. Based on an interview with the artists, Sarah Chaker summarises in this article their thoughts towards this special kind of Musikvermittlung, which was retrospectively described by many of those present as particularly innovative and expedient.
Interview: With Anouk Ben-Tchavtchavadze, Senior UX Designer at Riot Games

Resources are generally limited, and sacrifices and compromises need to be made regularly, and this often means cuts to scope in some form or another. Things that are not deemed crucial get cut, trimmed, or postponed, such as accessibility features or usability issues. Some of the UX challenges will definitely be different, where on console, while working on Far Cry, a lot of the work people did was focused on player feedback, from health to minimaps, contextual menus, and optimizing for an immersive and intense experience. Working on mobile, usability, iteration, and testing were much more prevalent to help craft for more casual experiences that users could pick up at any time. Each game, whether it will be mobile or console, live or not, casual or core, will have a different audience and different problems to solve for.
Bridging boundaries between Systemic Functional Linguistics and translation studies1

In Chapter 3, Erich Steiner discusses his motivation of studying linguistics and introduces his work from different perspectives in SFL and translation, including machine translation in the 1980s, corpus-based research, register, explicitation, grammatical metaphor, integration of product- and process-based research, and language description and comparison. He also relates the application of SFL to translation studies, covering topics such as SFL and other functionalist theories of translation, the tools for translation contributed by functionally oriented work and translation as a method for language teaching. In addition, he summarizes the contributions of SFL to translation and suggests some possibilities for future research.
Teknologi Baru Pada Pendidikan Tinggi Menuju Revolusi Industri 4.0: Studi Kasus Indonesia dan Malaysia

IoT with E-learning is intended to support data collection from devices and share to other devices in use for effective E-learning applications from Smart Campus. This study aims to conduct studies related to online learning models by utilizing Internet of Everything (IoE) technology to support Revolution 4.0. This study aims to support the latest communication paradigm in which the objects of everyday life will be equipped with a series of appropriate protocols and enable them to communicate well with each other as part of the internet. IoE will help improve learning by leveraging the large subject data generated by these objects to provide dynamic services to educators, learners, and even content developers. Using qualitative research methods This research uses a questionnaire to find out the views and assessments of the community in this case online learners regarding online learning as one of the impacts of the Covid-19 pandemic and produces an online learning model that is supported by an integrated system between learning media such as LMS and devices. others use IoE. The results of this study support the implementation of Smart campuses that allow the use of IoE methodologies to make them always ready in certain network areas.
Dynamic Image Monitoring Method of Hitting Point for Tennis Players in Serving Instant

In order to realize the dynamic monitoring ability of the hitting point for the tennis player in serving instant, thereby improving the serve quality of the tennis player and guiding the improvement of the serve action, a dynamic image monitoring method of hitting point for the tennis player in serving instant is proposed based on multi-mode registration and key contour feature point detection. Adaptive noise reduction processing is carried out on the dynamic image of hitting point for the tennis player in serving instant, the feature point and contour information of 3D hitting point’s dynamic image are extracted with Harris corner detection method, and the instant hitting action of tennis player is reconstructed. The envelope detection method is used for dynamic image’s multi-modal registration, and the key contour feature point detection method is used to reconstruct and monitor the dynamic image of the instant hitting point of tennis ball. The simulation results show that the proposed method is better in real time and high accuracy in detecting the key feature points of tennis players, and the performance of image recognition and reconstruction is improved.
Integrating process-based research and machine translation with Systemic Functional Linguistics

In Chapter 7, Adriana Pagano first introduces the developments that have occurred in translation process research. Then she discusses the advantages of combining translation process research with text analysis based on SFL as well as the cooperation between Erich Steiner’s team at Saarland University in Germany and Fabio Alves’s team at the Federal University of Minas Gerais in Brazil. She also highlights the latest development in translation process research and talks about her future plan of works in this area as well as the studies that need to be done.
Mental Health Helper: Intelligent Mobile Apps in the Pandemic Era

Every human being faces various episodes of events that can cause changes in mental health conditions, including this coronavirus pandemic disease. The ups and downs of the psychological turmoil dynamics resulted, and also traumatic feelings can occur continuously or for a certain period. It can cause an adverse response for those who experience it and even cause anxiety or mental disorders. The implementation of restrictions on community activities during these pandemic circumstances makes people who want to check their mental health condition difficult to meet the experts or professionals such as psychologists. Therefore, the application that can detect these anxiety disorders as early as possible to minimize unwanted effects was developed. In the making of the application, an expert system is used to determine the results of the diagnosis. The expert system requires knowledge that is produced from experts, especially in the psychology field. The data that has been obtained will be processed and then yield the results determined from the classification of anxiety types using several methods in Artificial Intelligence. Several tests were carried out 50 times using Certainty Factor methods to obtain an accuracy rate of 96%. It has similar accuracy compared to the NaÃ¯ve Bayes method. This application called Mental Health Helper has a validity and reliability test to prove that this application is valid and reliable. It has better performance than previous researches, which still only has two classes of diseases.
Introduction to Visual Memory

Our memories are an intricate mix of rich perceptual and conceptual information. As we engage with the world, we are constantly creating new memories of what we see, and reflecting on events we have seen in the past. However, although both visual perception and memory have long traditions of study in psychology and neuroscience, there are still large open questions about how an event goes from being seen to remembered and what visual information remains in our memory traces. Just in the last decade, we as a field have made large strides in understanding visual memory, thanks to a renaissance of new theories, frameworks, and methods.
Network Security Management and Protection in the Context of Emerging Technologies

Digital technologies have expanded the reach of financial services to end-users, improved efficiency and driven global financial innovation to a new level. It has created new directions and opportunities for the transformation and development of the financial industry, but at the same time it has also brought new problems of network security management. In this paper, we study the cybersecurity issues of the financial industry in the context of emerging technologies. The results find that the high-benefit, regulated and high-risk characteristics of the financial sector have led to the prominence of financial data security issues, a significant increase in cyber attacks and more insidious cyber fraud in the financial sector. To this end, it is proposed to implement the network security ecological assessment system as soon as possible, to conduct regular network security attack and defense drills, to strengthen the application of technology in network security, to focus on key areas to carry out publicity activities and other protective measures to build a solid financial network security “firewall”.
Epilogue

In Chapter 9, Bo Wang and Yuanyi Ma introduce the important studies that apply SFL to translation and summarize the contributions of SFTS. Based on the discussions in the previous chapters, they also suggest some directions for future research.
Machine Translation Utilizing Similar Translation Retrieval

Neural Fuzzy Repair (NFR) system enables an NMT system to improve translation accuracy using similar translations searched from Translation Memory. This paper compared edit distance and sentence-BERT (SBERT) as the similarity measures used in the search for similar translations, and showed that SBERT outperformed edit distance in the case of small corpus sizes. This paper also studied a method to automatically select the most appropriate translation from more than one candidates. Compared to the naive method based on the number of tokens, the method based on the inner product of SBERT’s sentence embedding achieved significant improvements. These results prove the effectiveness of the SBERT-based approach in the NFR system.
Path Planning for Robotic Training in Virtual Environments using Deep Learning

Reducing costs in the acquisition of industrial robots and their benefit in continuous production workdays have increased the number of investigations that expand the robot's action capabilities. This work proposes a trajectory planning system for a UR3 robot that is very usual in academic, research, and industrial applications. The system is presented based on a convolutional network training for regression tasks focused on learning the desired trajectory. A virtual environment has been developed to simulate different trajectories based on the interaction between UR3 robot and object detection and location through the convolutional network employed. This work exposes the network's training and the results of the transport of the object, where the robot can position itself on the desired tool (scissors and screwdriver), which is recognized by training a Faster network R-CNN and the re-localization of the tool in a conveyor band. For the trained trajectory’s, a ResNet-50 model is proposed, and the overall performance achieved was 92.63%, with a mean square error of 24.7 mm in the trained trajectory's repetition. Also, the boxplot of each ax in the trajectory is exposed since they show in a more detailed way the deviation of each of the points in the whole validation set. The average collection time, from when the system takes the workspace capture to its initial positioning after leaving the tool on the belt, was 51.3 seconds, enough for real-time applications.
An XML Privacy-Preserving Data Disclosure Decision Scheme

In order to protect the sensitive data represented as XML documents in a trusted collaborative system where sensitive data are not shared, an XML privacy-preserving data disclosure decision scheme was proposed under the assumption of a trusted server. This scheme is inspired by the idea of separating storage structure and content. Temporary access matrix is used to represent structure authorization and the vector represents the content authorization of leaf node. According to the conversion rules, access matrix not only represents access authorization of all nodes but also keeps the main structure of the XML document. With the combination of the vector and matrix, it can provide different access views for different group users with different purposes. In addition, start-end encoding is used to encode all the nodes for locating nodes and the content; privilege matrix solves the problem of privacy synchronization change for all users. At the same time, authentication polynomials are used to verify different users and improve the security level. The experimental results show that the scheme not only effectively protects XML sensitive data but also reduces the storage pressure on the server side; at the same time, from the response time, we know that it is beneficial for the rapid search and information positioning.
Corrigendum: SVInterpreter: A Comprehensive Topologically Associated Domain Based Clinical Outcome Prediction Tool for Balanced and Unbalanced Structural Variants

[This corrects the article DOI: 10.3389/fgene.2021.757170.].