Reviewerly/exHarmony huggingface dataset:
Multilevel Consensus Blockchain Algorithm for Digital Forensics on Medical Data During the COVID 19 Situtaion

This chapter discusses a novel approach to healthcare and the medical field by incorporating blockchain technology. During the COVID 19 situation, the medical field encountered difficulty in managing medical records and gathering accurate data, which seemed to be quite challenging to governments. The proposed method can be used to manage medical data, in particular to connect all governments or private hospitals, securely transfer the data, and focus on digital forensics. Similarly, this proposed method is used to track patients, hospital details such as number of beds, medicine and patient treatment, so that governing bodies can access accurate details by following the necessary steps to serve the people. This method helps in managing medical record data that is more helpful and secure. In the case of any fraud detected in the records, the forensics team can investigate effortlessly. Fraud cannot meddle with the data due to the security provided by a blockchain. A multilevel consensus algorithm is used for adding transactions in the blockchain distributed network.
What is it, I do(n't) not know? I (we) do not know (but will feel) the name

In this article Britton and Paehr play with language around concrete, which makes-firm in language ("Please make this more concrete!") and hardens the grounds that feet, canes and wheels touch every day. Through three patched, sprouted and decomposed collages of concrete material experiments, attempt to unsettle concrete's supposed firmness. N. N. is that which has not been named. In these images N. N. is not concretized and remains open. We are finding ways of making other worlds possible that resist the foreclosing of access or possibilities. The work of these authors germinates with the crip technoscience manifesto from Aimi Hamraie and Kelly Fritsch where access is defined as both related to attack and contact (2019). Instead of the 'integration' of disabled body-minds into normative space, Hamraie and Fritsch uphold the ways in which disabled makers hack or make otherwise trans*feminist presents. Hacking and playing with concrete, we find moments of formlessness through patching, decomposing and sprouting. The authors discover access potentials within something that has been figured as hard, and yet is ontologically, metaphorically and physically not stable. By keeping concrete "open" by adding more water or other materials to interrupt the "curing", and by churning/stirring this mixture, sprouting, decomposing and patchworking operate as practices that resist hardening, firming, closing and foreclosing. N.N. (nomen nomiandum) holds things open and resists formation because, I may not know, but together we can access it.
Research and Dissemination in the Language Teachers’ Associations Publications The Case of <i>SeLM. Scuola e Lingue Moderne</i>

In this article, we briefly review the main stages of the evolution of language teaching in the Italian school system and the parallel development of pedagogical journals. Then, we will focus on the roles and functions of the journals of language teachers’ associations, analysing their characteristics and developments, also in light of recent technological innovations. The specific case that we will examine will be the journal SeLM. Schools and Modern Languages , edited by ANILS, the Italian National Association of Teachers of Foreign Languages.
Relevance of the Problem as Life Experience: A Phenomenological Perspective on Scholarly Publications

The issue of preparing scholarly articles for publication in highly indexed journals has been acute for the last five or six years, since scientometric indicators became the main tool for evaluating the performance of both universities and representatives of the academic community. However, no definitive answer or algorithm for lining up the process of scholarly article preparation has yet been found. It seems that we need to start with the basics: a philosophical methodology that probes into the nature of scientific communication. This paper analyses the application of the phenomenological philosophy method to solve the indicated problem. The author used theoretical works of the founders of the phenomenological tradition in philosophy and researchers developing the idea of intersubjectivity in conveying meanings during communication, including scientific communication. Further, the internationally accepted IMRAD article format is applied as a theoretical justification for the logical organization of the research text. The phenomenological method describes in detail the influence of personal biography on the organization of the communication process. The nature of the latter is such that the meaning of a statement becomes clear to the communicative partners only after a common system of reference has been developed. Hence the need to include in the communicative message, such as a scientific article, the functional elements of background knowledge and the relevance of the research problem. Thus, the study provides a theoretical substantiation of the importance of structuring scholarly papers according to the scientific publication format.
Cascade detector for oriented airplane detection in remote sensing images

Abstract Although the current target detection field has made remarkable progress, airplane detection in large field optical remote sensing images is still challenging. To this end, we innovatively propose an oriented airplane detector to solve the two difficulties. In response to the slow detection speed of optical remote sensing images, we use an anchor-free structure to avoid complex calculations related to anchor boxes and significantly reduce the use of memory and time in computing. Since the airplane target are distributed with arbitrary orientation, the accuracy of the general detector is reduced. We alleviate this problem by replacing the standard convolution with asymmetric convolution. In addition, we use a cascade structure in prediction, which can further improve accuracy with almost no increase in the calculation. Abundant experiments and detection results show our methods effectiveness. In airplane detection task, our proposed model achieves 88.14 mAP on DOTA-v1.0 and 90.46 on UCAS- AOD.
Determination of diffuse and direct irradiance under cumulus clouds using pyranometer data.

In this paper, the authors propose a method for determining diffuse and direct irradiance under cumulus clouds using an unshaded pyranometer data, and provide an example and practical measurement results.
Exploration Tool for Effectively Interpreting the Visual Metaphor Process of Sentiment Visualization

This study proposes an exploration tool for users' intuitive understanding of visual metaphor processes in sentiment visualization cases. To create the exploration tool, we conducted the following procedure 1) Extract sentences about visual metaphor and use ‘Target, Intermediation, Representation, Visual Variables, Visualization Technique’ as a taxonomy to analyze metaphor processes in the collected cases. 2) Create Network to suggest the relationship between representation and intermediation. 3) Utilize Sankey Diagram to perceive the frequent metaphor process patterns in stages.
SpEakWise VR: exploring the use of social virtual reality in telecollaborative foreign language learning between learners of English and German

This paper discusses the potential of immersing foreign language learners in Social Virtual Reality (SVR) in an international seminar, designed by the authors, called SpEakWise VR, combining the tandem language learning paradigm with gamification in immersive virtual environments. SpEakWise VR builds on an existing telecollaboration (SpEakWise) between undergraduate students in Trinity College Dublin and Hildesheim University. We set out to explore how inclusion of an SVR activity influences student engagement, learning involving intercultural and multilingual problem solving, and team building. This paper presents initial observations from our research, concerning multimodality in embodied immersion and code-switching, and proposes future avenues of inquiry.
An approximate randomization-based neural network with dedicated digital architecture for energy-constrained devices

Abstract Variable energy constraints affect the implementations of neural networks on battery-operated embedded systems. This paper describes a learning algorithm for randomization-based neural networks with hard-limit activation functions. The approach adopts a novel cost function that balances accuracy and network complexity during training. From an energy-specific perspective, the new learning strategy allows to adjust, dynamically and in real time, the number of operations during the network’s forward phase. The proposed learning scheme leads to efficient predictors supported by digital architectures. The resulting digital architecture can switch to approximate computing at run time, in compliance with the available energy budget. Experiments on 10 real-world prediction testbeds confirmed the effectiveness of the learning scheme. Additional tests on limited-resource devices supported the implementation efficiency of the overall design approach.
Development of Design Principles to Author AR in Education Based on Teacher's Perspectives

The lack of AR authoring tools thought from the educational perspective is one of the reasons that hinder Augmented Reality’s widespread use in education. This work investigates important characteristics for AR authoring in education in the form of design principles. We aimed to identify how teachers would like to create AR experiences based on their pedagogic needs through Design Based Research (DBR) with the participation of an interdisciplinary team. We followed the 4 steps of DBR in 2 cycles. We worked with teachers to understand their educational needs when authoring AR experiences. Then, we defined and prototyped a case study focused on language for children and teenagers, which we evaluated in two test rounds. As a result, we proposed 11 design principles divided into three aspects: infrastructure, augmented reality, and pedagogy, which were evaluated positively by teachers.
A Method for Assessing the Thickness of Cobalt-Rich Crust on Seamounts and Its Application on the Il’ichev Guyot

Seamount cobalt-rich crusts are rich in cobalt resources and are sought after worldwide. Among different affecting parameters, crust thickness is the most important in evaluating cobalt-rich crust resources in seamounts. Generally, there are two challenges to crust thickness evaluation: firstly, due to high operating costs, most geological stations for seamount exploration have sparse sampling distributions so there are insufficient data to estimate the crust thickness distribution; secondly, a single evaluation method has advantages and disadvantages, and it is not feasible to benefit from the advantages only. These methods cannot simultaneously make full use of the sampling data in local areas, providing a more appropriate evaluation of the whole area. As a result, the estimated results cannot fully reflect the thickness distribution. Based on the thickness data of the station survey and topographic data, geostatistical units are divided, and a comprehensive crust thickness assessment scheme is established on the ArcGIS platform. To this end, the adjacent area method is applied to calculate the crust thickness within the influence range of the station. Combined with the station buffer radius and Thiessen polygon method, the crust thickness within 1.5 km of the survey station was estimated. Then the “slope–distance” Kriging interpolation method was used to calculate the crust thickness in the study area, and the crust thickness in the optimal effective radius area was given to compensate for the missing part in the first step. Finally, the geological blocks were divided using the topographic classification method, and the crust thickness of the remaining unassigned regions was estimated using the mathematical expectation method. The proposed method was applied to evaluate the Il’ichev Guyot’s crust thickness and reasonable results were achieved. It was found that the thickness estimation of the area near the station is consistent with the measured values. Since finer topographic data are used in the calculation, the thickness estimation result is more detailed. In this regard, a simple and effective calculation method was established on the ArcMap platform. The mathematical expectation estimation method of the crust thickness, based on the topographic and geomorphological classification from the perspective of the mineralization mechanism, compensates for the drawbacks of the first two methods originating from the lack of data points. The results show that the proposed method is an appropriate scheme to evaluate seamount crust thickness without comprehensive investigation.
SCMBQA: Design of a Customised SCM-Aware Sidechaining Model for QoS Enhancement under Attack Scenarios

Storing&amp; processing data for supply chain management (SCM) systems requires design of high-security and quality of service (QoS) aware models. These modelsassist in improving traceability performance of SCM deployments via integration of transparent &amp; distributed mechanisms. A wide variety of security models are proposed by researchers to perform these tasks, and it is observed that blockchain-based SCM implementations outperform other models in terms of security &amp; QoS metrics.But most of these implementationsare general-purpose and do not incorporate SCM-specific consensus &amp; mining rules. It is also observed that, mining speed&amp; throughput performance of these blockchain-based implementations reduces exponentially w.r.t. number of SCM transactions. To resolve these issues, this paper discusses design of a novel Proof-of-Supply Chain (PoSC) based consensus model, which is specifically designed for sidechain based SCM deployments. The PoSC consensus model is used for high-efficiency SCM-based data storage and communication scenarios. The proposed PoSC consensus model is capable of resisting selfish mining, time jacking, and sybil attacks, which are targeted towards SCM deployments. The model uses temporal performance metrics of miner nodes, and combines them with relationship graphs to form an SCM miner rank. Based on this rank, miner nodes are selected, and their consensus responses are recorded. These responses are processed using an augmented deep learning model, that is trained over 8 different SCM implementations via machine learning. After successful mining, responses obtained from these miners are used to incrementally train the machine learning model which assists in continuous performance improvement. The SCMBQA model was tested on milk supply chain, agriculture supply chain, and electronic supply chain applications, in terms of computational speed, throughput, energy requirement, retrieval &amp; verification delay, and storage requirements. It was observed that the proposed PoSC consensus was capable of improving the computational speed by 8.5%, reduce energy consumption by 4.9%, improve throughput by 9.6%, and reduce storage costs by 15.4% when compared with standard blockchain-based SCM consensus models. This is because the proposed model deploys an intelligent sidechaining approach, that is capable of optimizing number of generated sidechains via temporal QoS &amp; security performance metrics. Due to use of smaller chain lengths, the proposed model is capable of integrating privacy-aware &amp; secure approaches depending upon different SCM stages. Thus, distributor-level security models are different than retailer-level security models, which assists in context-sensitive block deployments. Due to use of PoSC, the proposed model was observed to be 99.5% resilient against internal and external attacks, which makes it useful for real-time SCM deployments.
Advancements in Reversible Data Hiding Techniques and Its Applications in Healthcare Sector

Among all the approaches, Digital watermarking is the most widely implemented approach for copyright protection and authentication of data. In this technique, a unique piece of information is known as a watermark. Then the watermark gets into an image, later, to achieve its objective the watermark will be extracted. For the transmission of medical images, digital watermarking schemes are mostly used to ensure that the image has not gone through any unauthorized or illegal modifications during the transmission. Since conventional watermarking schemes alter the pixels in the original image, it is not suited for watermarking medical images. In medical images, permanent modifications may adversely affect the diagnosis process at the receiver side, caused by watermarking, especially when we are using some computer-aided diagnosis tools. This motivated computer scientists to work on reversible watermarking schemes. The reversible watermarking technology makes it possible to recover the required medical image from the watermarked image, while extracting the hidden watermark. So, the reversible watermarking technique does not affect the diagnosis in any way since the recovered image will be equivalent to the original image. This recovered image will be used by the user. The use of reversible watermarking techniques to send patient reports along with medical images is also explored, with the patient reports being embedded in the medical picture itself rather than the watermark. These techniques are commonly known as reversible data hiding techniques. This book chapter gives a brief overview of reversible data hiding techniques, reversible watermarking methods, and the major applications in medical image transmissions. In addition, the chapter addresses contemporary reversible data hiding and reversible watermarking algorithms intended specifically for medical picture transmission. The chapter also discusses some of the obstacles that must be overcome when developing a reversible watermarking system for healthcare applications.
A Preliminary Study of The Ethical of The Spirit of The Chinese Women's Volleyball Teamfrom The Perspectiveof Traditional Chinese Taoism and Confucianism

The spirit of Chinese women's volleyball team in the new era, in which the motherland comes first, unity and cooperation, tenacity and perseverance, and never giving up, is a concentrated expression of the spirit of sports of the Chinese nation and a manifestation of the pursuit of the values of modern sports civilization. It is thought-provoking whether traditional Chinese culture has the ethical system to support the formation of the Chinese women's volleyball spirit. This study takes this as the base point and uses theories related to history, ethics, logic and culture, as well as literature and interviews, to analyse the traditional Chinese Confucian ethical system behind the spirit of women's volleyball. The study concludes that traditional Chinese Confucian ethics is closely linked to the spirit of Chinese women's volleyball, and provides the inner logic for the formation of the spirit of Chinese women's volleyball, the basic guidelines for the practice of the spirit of Chinese women's volleyball, and multiple forms for the expression of the spirit of Chinese women's volleyball. The inner logic of the spirit of Chinese women's volleyball team is rooted in the Confucian idea of "establishing virtue, merit and speech"; the basic guideline for the practice of the spirit of Chinese women's volleyball is the Taoist core idea of "doing nothing and "Defending the center"; The spirit of Chinese women's volleyball is manifested in various forms, including a positive attitude towards life, respect for the elderly, respect for teachers and a sense of righteousness in the face of profit, the pursuit of the spirit of "If a man in the morning hear the right way, he may die in the evening without regret", and a sense of national responsibility to "love the motherland and build a successful career".
BT+ Approach to the Meaning Construction of Metonymy-Based Chinese Cyber-Expressions

China has the biggest population of netizens in the world and more and more cybernauts often use cyber-expressions in their Internet communication and some cyber-expressions are even used in our daily life offline. With the increasing popularity of these cyber-expressions, they have caught the eyes of more and more scholars and these expressions’ formation and meaning construction are analyzed from many perspectives. From the perspective of Cognitive Linguistics, the Chinese cyber-expressions are formed with the help of such cognitive mechanisms as metaphor, icon iconicity, metonymy and metaphtonymy. While the metonymy-based e-terms account for a large proportion of all the new cyber-expressions, little is discussed about metonymy-based expressions in previous studies related to the Chinese cyber-expressions. In the present paper, 752 metonymy-based e-terms from a closed corpus will be studied exhaustively and the meaning construction of these kinds of expressions will be systematically analyzed based on the Blending Theory under the framework of Cognitive Linguistics with an attempt to fill the gap in the previous studies.
Graph Neural Network Based Living Comfort Prediction Using Real Estate Floor Plan Images

In recent years, machine learning has been widely used in the real estate field. However, most of these previous studies have been limited to analysis based on objective perspectives, such as analysis of the structure of the floor plan and rent estimation. On the other hand, we focus on the subjective "living comfort" of real estate properties and aim to predict people's impressions of properties based on information obtained from floor plan images. Specifically, by using deep learning to analyze floor plan images and graph structures reflecting the floor plans, it becomes possible to predict the attractiveness of each property in terms of spaciousness, modernity, privacy, and so on. As a result of the experiments, the effectiveness of using both the floor plan image and the corresponding graph structure for prediction was confirmed.
Statistical Tests for Comparing Machine Learning Algorithms

Abstract: The average result of machine learning models is determined using naive 10-fold cross-validation, paired Student's ttest, McNemar's test. The algorithm with the best average performance should outperform those with the worst. But what if the difference in average results is due to a statistical anomaly? A statistical hypothesis test is used to determine whether the mean results of the differences between the three algorithms are real. Using statistical hypothesis testing, this study will show how to compare machine learning algorithms. When choosing a model, the output of several machine learning algorithms or simulation channels is compared. The model that performs best based on your performance measure becomes the final model that can be used to predict new data. With classification and regression prediction models, this can be done using traditional machine learning and deep learning methods. It is difficult to determine whether the difference between the two models is accurate or not.
Quantum transfer learning for image classification

Quantum machine learning, an important element of quantum computing, recently has gained research attention around the world. In this paper, we have proposed a quantum machine learning model to classify images using a quantum classifier. We exhibit the results of a comprehensive quantum classifier with transfer learning applied to image datasets in particular. The work uses hybrid transfer learning technique along with the classical pre-trained network and variational quantum circuits as their final layers on a small scale of dataset. The implementation is carried out in a quantum processor of a chosen set of highly informative functions using PennyLane a cross-platform software package for using quantum computers to evaluate the high-resolution image classifier. The performance of the model proved to be more accurate than its counterpart and outperforms all other existing classical models in terms of time and competence.