Reviewerly/exHarmony huggingface dataset:
Examining Bitcoin Price Volatility Transmission between Spot and Futures Markets

This paper examines volatility transmission between the spot and futures bitcoin markets. We use daily series over a sampling period that spans from December 2017 to September 2022. We examine several events that spread severe risk in cryptomarkets, such as the COVID-19 pandemic in 2020, governmental announcements and environmental concerns in 2021, and crypto-winter cases in 2022. We calculate symmetric and asymmetric VIRFs using a VEC-BEKK-GARCH model and the Hafner and Herwartz (2006) framework. Our results from the VIRF analysis demonstrate the existence of asymmetric responses between the two markets, with the shock during the COVID-19 pandemic having a greater impact on the variance of the futures market. Additionally, we employ the connectedness approach of Diebold and Yilmaz (2012, 2014) to examine the volatility spillovers across the two markets. Our results suggest that the bitcoin spot market is the dominant transmitter of volatility shocks to the futures market.
Politeness Strategies Applied by the Characters of Princess Diaries Movie

This study examines the politeness strategies of the characters in the Princess Diaries movie. The data were utterances of Mia and her grandmother, the main characters in the Princess Diaries movie. The analysis is based on Scollon and Scollon's politeness theory. As a result, the researcher found that Mia and Her Grandmother employed two types of politeness: involvement and independent strategies. Mia used twenty politeness strategies, including four involvement strategies and sixteen independent strategies. Her grandmother, on the other hand, employed fifteen politeness strategies: twelve involvement strategies and three independent strategies. This study may inspire additional researchers to investigate politeness strategies not only in two characters but also in various films, theories, or methods. Additionally, it is hoped that research on politeness strategies will expand our understanding of how linguistic aspects, particularly in English, are applied.
Data Practices and Management

Abstract Employing good data management practices is important for enhancing the transparency and validity of research, as well as the reproducibility of research findings. This chapter aims to help early career researchers translate the European Code of Conduct for Research Integrity principles and guidance on data management practices into everyday research. In this chapter we will guide you on data practices and management throughout the lifecycle of research data – data management planning, organizing and storing data, preserving and sharing data, reusing and citing data. You will also learn about the data management procedures relevant to each of the data lifecycle phases – preparation of data management plans, procedures for storing data properly and securely, examples of repositories for preserving and sharing data, licenses for reusing data, etc. The chapter will also outline the FAIR data principles and data protection requirements and safeguards important when handling personal data in your research (GDPR requirements, pseudonymization, anonymization, and deleting data).
UniQGAN: Towards Improved Modulation Classification with Adversarial Robustness Using Scalable Generator Design

Automatic modulation classification (AMC) has been envisioned as a significant element for security issues at the physical layer due to its indispensable role in accurate communications. Recent attention to deep learning has impacted the AMC, which exhibits exceptional performance without manual feature engineering. To guarantee the accuracy and robustness of deep learning-based AMC, data augmentation is a critical issue. While existing studies have used several deep generative models to handle the data insufficiency, these studies face three challenges including low scalability, lengthy training time, and limited accuracy improvement. To this end, this paper presents UniQGAN, a novel unified generative architecture that models I/Q constellation diagrams from various signal-to-noise ratios (SNRs) using a single model. The proposed method enables the generation of high-quality data with a scalable generator, while requiring reduced training time. At the core of UniQGAN are <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">multi-conditions embedding</i> and <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">multi-domains classification</i> techniques that leverage both SNR and modulation type during the optimization process to enable unified modeling. Using abundant high-quality training data, UniQGAN accelerates the enhanced AMC with high performance and adversarial robustness. Experimental results demonstrate that the data generation by UniQGAN achieves superiority in terms of scalability, training time, and accuracy.
Implementation of a workbench platform for the management of smart contracts in BlockChain nodes on Azure Cloud

Objectives: Analyze, design and build backend components (web API, XML web service and database) and frontend (website) with Microsoft technology that allow the integration of a functional workbench platform to automate the launch of compiled smart contracts in blockchain nodes hosted in Azure Cloud so that they can be used by companies in general. Methodology: The SCRUM methodology was applied for the agile development of the technological products of the backend and frontend, because it adapted very well to the nature of this research, since in each iteration there was always an update of the tools, programming components, testing and configuring services to achieve goals. Contribution: It is shown that the development platform integrated by the solidity language, Azure cloud blockchain, the Visual Studio .Net IDE and the SQL Server database manager allowed to design and build a blockchain workbench platform easily exploitable by companies to validate their processes and generate transparency in the handling of their information.
Optimizing Parameter Mixing Under Constrained Communications in Parallel Federated Learning

In vanilla Federated Learning (FL) systems, a centralized parameter server (PS) is responsible for collecting, aggregating and distributing model parameters with decentralized clients. However, the communication link of a single PS can be easily overloaded by concurrent communications with a massive number of clients. To overcome this drawback, multiple PSes can be deployed to form a parallel FL (PFL) system, in which each PS only communicates with a subset of clients and its neighbor PSes. On one hand, each PS conducts iterations with clients in its subset. On the other hand, PSes communicate with each other periodically to mix their parameters so that they can finally reach a consensus. In this paper, we propose a novel parallel federated learning algorithm called Fed-PMA, which optimizes such parallel FL under constrained communications by conducting parallel parameter mixing and averaging with theoretic guarantees. We formally analyze the convergence rate of Fed-PMA with convex loss, and further derive the optimal number of times each PS should mix with its neighbor PSes so as to maximize the final model accuracy within a fixed span of training time. Theoretical study manifests that PSes should mix their parameters more frequently if the connection between PSes is sparse or the time cost of mixing is low. Inspired by our analysis, we propose the Fed-APMA algorithm that can adaptively determine the near-optimal number of mixing times with non-convex loss under dynamic communication conditions. Extensive experiments with realistic datasets are carried out to demonstrate that both Fed-PMA and its adaptive version Fed-APMA significantly outperform the state-of-the-art baselines.
A Simultaneous Multi-Directional Defects Measurement Based on Polarization-Multiplexed Four-Directional Digital Shearography With Large Field of View

Since the sensitivity of digital shearography (DS) is related to its shearing direction and the anomaly of the relative deformation is sensitive to the defect direction in non-destructive testing (NDT), traditional single or two direction DS systems may miss defects in some directions. To avoid missing defects, a polarization-multiplexed four-directional digital shearography for simultaneous measurement of multi-directional defects is proposed in this paper. Four object beams whose relative shearing amount and spatial carrier frequency can be adjusted independently are constructed, with which four shearograms can be obtained simultaneously in one recorded interferogram. And by embedding a 4f system in front of the interferometers to transmit the captured image, the measurement field of view (FOV) is doubled. To verify the performance of the proposed four-directional DS system, an experimental setup was constructed, and two experiments of central force loading and non-destructive thermal loading were performed. Experimental results verify that the proposed system can effectively detect the defects in multiple directions simultaneously.
Classification Model for Identification of Internet Loan Frauds Using PCA with Ensemble Method

Due to the rapid growth of e-services including e-commerce, e-finance, and mobile payments, personal loans for consumption have seen a significant uptick in recent years. There will be massive losses due to credit loan fraud because of insufficient grid checking and monitoring. Because of the time and effort required to manually analyse and verify such a high volume of credit/debit card swaps, machine learning methods should be widely deployed to automatically identify fraudulent transactions. This paper offers a unique hybrid supervised & unsupervised learning approach, P-XGBoost that combines Principal Component Analysis (PCA) and XGBoost (Algorithm) to filter out superfluous data while keeping significant information. There, use system search to prevent over-fitting and investigate the showcase of XGBoost, P-XGBoost, and other conventional AI technologies. It comes out that P-XGBoost completely surpasses XGBoost when it comes to detecting fraud, and it also offers a completely novel viewpoint on maintaining client anonymity while searching for forgeries behaviour.
Issues in Cloud Load balancing Fault-Tolerance: Review and Challenges

Load-balancing is critical in cloud-computing for optimizing workload distribution and making the most use of available resources, which in turn speeds up the system's total response time. There have been several tactics and methodologies proposed to handle load balancing-related issues such as task scheduling, migration, resource use, and others. This study offered numerous solutions to the crucial problem of load-balancing in cloud-computing. The difficulties regarding Load-balancing been examined by contrasting the methods put forth by researchers during the last six years. Many techniques have been proposed, however some issues, such as VM migration and unsolved fault tolerance issues, continue in the cloud setting.
A Secured Method for Authentication of Data Security using Public Key Cryptography

This study suggests that the cloud is an internet storage tool mostly used to store papers, media, and different types of material. However, because cloud storage is so widespread and accessible, there are a number of security concerns, including data theft and problems with authentication when sharing data on public platforms. Attribute-based encryption allows for the fine-grained exchange of encrypted data. This is done by utilizing a methodology to combine key creation, encryption, and decryption to guarantee data confidentiality and integrity. Data sharing in the cloud can be done using a variety of methods, each of which uses a unique set of stages or processes to get the job done. One of those methods is public key encryption, a type of asymmetric key encryption in which the data or document is protected by both the public and private keys. Our challenge is to create an efficient public-key encryption scheme which enables flexible delegation in the sense that any subset of the cypher texts (made by the encryption scheme) is decryptable by a constant-size decryption key (issued by the owner of the master-secret key). By utilizing a unique form of public-key encryption, which is referred as the key-aggregate cryptosystem to resolve this issue.
A Review on Unconstrained Real-Time Rotation-Invariant Face Detection

With the amazing growth of image and video databases, there is a vast need for intelligent systems to automatically understand and look at information since doing it by hand is getting very hard. Faces are significant in social interactions because they show the feelings and identity of a person. People are not much better than machines at recognizing different faces. The automatic face detection system is a key in head pose tracking, face verification, face recognition, face tracking, face animation, face modeling, facial expression recognition, age and gender recognition, and behavior analysis in a crowd. Face detection is a way for a computer to find out the size and location of a face in an image. Face detection has been an outstanding issue in computer vision literature. This paper provides an overview of pose and rotation invariant face detection approaches with architecture designs and performance on popular benchmark datasets. The benchmark datasets used for face detection are listed as their key features. This paper also talks about different applications and challenges with face detection. Also, we set up special discussions on the practical aspects of making a face-detection system that works well. We end this paper by suggesting a few promising directions for future research.
Breast Cancer Classification Techniques – An Investigation

According to statistics around the world, breast cancer is one among the diseases that leads to fatal death, and the way to protect human life is early detection. In recent years, the ratio of death rate caused by this cancer has increased. In recent years, 30% of cases have been reported in India, making breast cancer one of the most common types of women. For every 2 min, a woman is diagnosed with this cancer in India, and every nine minutes a woman eventually passes away from breast cancer in India. Breast cancer early detection can significantly increase patients’ odds of surviving by elevating their access to appropriate medical treatment. Identifying whether a cancer is benign or malignant is the main objective. The deep learning model plays a major role in classification and detection applications. It reduces the complexity of the image and the number of layers present in that input image. In this paper, the extensive study of the current technique for detecting the breast cancer is discussed. Issues and challenges of the existing system are also covered.
Emotional Text Generation with Hard Constraints

Transformer-based pre-training language model can generate high-quality natural language. This kind of language model has also achieved good results in the task of generating emotional text However, most of the existing emotional text generation methods use soft constraints to guide the text to output the target emotion and focus on controlling the emotional attributes of the whole text, but there is still a lack of hard control over the aspect emotion at the word and phrase level In order to solve the above problems, this paper proposes an emotional text generation method under the restriction of hard constraints Firstly, aspect emotion analysis is used to extract the aspect words and emotion words of the sentence, and then the aspect words and emotion words of the target emotion are selected as the hard constraint input of the pre-training language model to reconstruct the complete sentence. In this paper, a new word weight calculation method is designed to make the model generate important words first Experimental results show that the sentences generated by this method not only have aspect-level emotion, but also significantly improve the evaluation indexes of text quality and diversity.
DCPowerMan: A Power Management System for Data Center

In order to make full use of the power resources in the data center, the data center power oversubscription scheme is generally adopted, which can increase the power efficiency and make full use of the cabinet space to deploy more computing resources. However, this solution will cause security risks. When the servers in the cabinet reach a high power level at the same time, the real-time power consumption on the power supply line will exceed the rated power consumption, causing the circuit breaker to trip, affecting the operation of the applications on the server and the users, or even burning down the server, causing irreparable damage. In response to the above problems, we propose DCPowerMan, a power management system based on (Average Runtime Power Limit) RAPL. When the server reaches a high power level, our system can give application migration suggestions and perform power management to reduce real-time power consumption and ensure power consumption safety. Experiments in a real computer room environment demonstrate the effectiveness of our system.
A Collection and Retrieval of Intelligent Teaching Data for Higher Vocational Finance and Economics Major Based on External Data Interface Optimization

Firstly, it analyzes the requirements of the high-speed data transmission system, and then gives the design scheme of the bridge chip based on PCI EXPRESSS, including the logic design block diagram, interface description and bus timing. Higher vocational finance and economics majors mainly train high-skilled professionals in front-line management and service, and their professional characteristics of management and service put forward unique requirements for students' operational skills, professional ability and comprehensive quality. Aiming at the current teaching status of this course and some problems in the teaching process, under the guidance of the virtual learning situation teaching method, the corresponding solutions are put forward through the task-based teaching of the course knowledge points. Different from full-text crawling, distributed directional crawler requires fast crawling of deep and precise structured data from the Internet.
Psychological training of a lawyer as the basis of professional practical activity

The article presents the results of the study of actual features of the psychological training of a lawyer as the basis of professional legal practice. The main practical personal psychological skills that a lawyer can develop and should use in the process of professional practical activity are considered. A perspective on the use of psychological knowledge in the educational process of a higher school for lawyers is offered. The conclusions state that the modern complex conditions of social development in our country make the profession of a lawyer particularly in demand. Since it is closely integrated into society, psychological preparation, understanding of laws, approaches, techniques and methods of psychology as a science and practice comes first in practical legal activity, in addition to acquired professional knowledge during training. A new quality of legal specialist training is not possible without taking into account and applying ideas, methods, practical approaches and analysis of situations inherent in the science of psychology. Knowledge of the psychology of a lawyer and other subjects of legal relations should become thorough in the system of training lawyers in higher education, which is increasingly becoming practically oriented.
Influence of Data Pollution on the Execution of Decisions in the Banking Sector of Pakistan

Purpose: The purpose of the study was to analyze the existence of information pollution in the banking sector of Pakistan. Worldwide unlimited and without restrictions information production and dissemination have resulted in an information-dense society and information-polluted environment.&#x0D; Methodology: An empirical study of 155 employees in the banking sector was undertaken. The influence of information quality and information quantity has caused information pollution. Eight factors of information quality were taken as independent variables. Decision-making is the key function of leaders and managers in the organization. Therefore, Decision making was taken as a dependent variable to gauge the influence of information pollution in organizations.&#x0D; Findings: The reliability and validity of data have been checked and results show that information influences a positive 37% influence on decision-making. According to the result, information pollution exists in the banking sector of Pakistan.&#x0D; Implications: It is further suggested to investigate the impact of information pollution on the work performance and productivity of organizations.
A Visualization Analysis of Literature on Blockchain Technology in Healthcare Applications With VOSviewer

Blockchain technology has been widely used in the field of healthcare. In order to investigate the current status, hot topics, and research trends of blockchain technology in healthcare application, this study uses VOSviewer visualization software and web database as the data base to present a systematic review of the blockchain technology application in healthcare industry. The aim is to help scholars catch up with research trends, find future research orientations, and research partners for further research.
Design and Implementation of Virtual Mouse With Volume/Brightness Control Using Hand Gestures

Hand gesture recognition system provides a natural, innovative and modern means of non-verbal communication. It has a wide application area in human-computer interaction and sign language. This paper proposes a way to control the position of the cursor, volume control and brightness control with the bare hands without using any electronic device. While operations such as clicking and dragging objects will be performed with different hand gestures, the proposed system will only require a webcam as an input device. The software required to implement the proposed system is OpenCV and Python. The output of the camera will be displayed on the screen of the system and will be further adjustable by the user. The python dependencies that will be used for implementing this system are NumPy, math, WX.
The Impact of Blockchain Technology in Healthcare

Nowadays, the healthcare systems are facing various problems in the fields of information security, data immutability, data sharing and management, trust, privacy of the patients and transparency. The management of data by the patients and healthcare professionals is complicated as the existing healthcare systems are centralized. Blockchain technology can digitalize and change the way data is managed with its decentralized and peer to peer network approach. In this way, the healthcare systems can benefit a lot from Blockchain technology. This paper helps to get an improved review of the recent works done on the Healthcare applications based on Blockchain.
Lightweight Two-factor Mutual Authentication for IoT applications

The advancement of diverse smart devices has led to emergence of applications such as smart healthcare, military, agriculture, etc. through which users can access data at any part of globe. These IoT networks are growing exponentially. This poses the challenge of authenticating remote users. This paper proposes a smart card based lightweight user authentication scheme for IoT deployment. Security analysis confirms security of our scheme. Furthermore, simulation results of our scheme in AVISPA validates its resilience to attacks. The scheme is lightweight, robust, and practically achievable in IoT deployment.