Reviewerly/exHarmony huggingface dataset:
Virtualization Revolution: Transforming Cloud Computing with Scalability and Agility

The realm of computing has undergone a remarkable metamorphosis, thanks to the revolutionary software innovation known as virtualization.This groundbreaking concept has completely transformed the utilization of computing resources, bringing about a paradigm shift in the way we approach IT operations.By abstracting and optimizing hardware resources, such as CPU, storage, memory, and network, virtualization empowers organizations to achieve enhanced efficiency and flexibility.At its core, virtualization allows the partitioning of server resources into multiple virtual machines or guests, leading to remarkable improvements in server consolidation, dynamic provisioning, and cost reduction.However, the influence of virtualization extends far beyond the domain of servers, permeating various facets of IT infrastructure.For instance, it has sparked significant advancements in memory virtualization, enabling systems to achieve higher performance levels by optimizing memory usage.Virtualization has also played a pivotal role in the rise of cloud storage, offering organizations scalable and adaptable storage options that align with their specific needs.Additionally, the advent of desktop virtualization has paved the way for the adoption of thin clients, while network virtualization has revolutionized network configurations through software-defined networking (SDN) solutions.The convergence of virtualization and cloud computing has been a transformative force in the IT landscape.Virtualization serves as the foundational pillar of cloud platforms, empowering organizations to efficiently allocate resources, seamlessly scale their operations, and provision virtualized environments on demand.This harmonious relationship between virtualization and cloud computing has redefined traditional IT infrastructure paradigms and service delivery models, propelling organizations toward greater agility and efficiency.In this article, we delve into the evolution and profound impact of virtualization on data centers and cloud computing, shedding light on the intricate interplay between these technologies and their implications for modern organizations.
Understanding governance and control challenges of blockchain technology in healthcare and energy sectors: a historical perspective

Purpose Despite rapid growth in blockchains, there was limited discussion about non-technical and technical factors on blockchain governance in the extant literature. This study aims to contribute new knowledge to the literature on potential factors affecting the adoption, governance and scale-up of blockchain technologies in the health-care and energy sectors, presented in a holistic framework. Design/methodology/approach This study adopts the qualitative case study research methodology to research blockchain governance in practice. The authors contacted a national blockchain consortium to conduct their research on the governance issue of blockchain. Two leading case organizations, one from the health-care industry and another from the energy industry, were deliberately selected for their study for their active role and reputation in the consortium and practical experience in blockchain governance. Findings The developed framework helps identify potential research gaps or concerns on adopting a blockchain as well as assessing blockchain implementation and governance in other industries. Depending on the circumstances, some of the factors can be either drivers or obstacles to further blockchain development. The different forces may also be more or less evident over time as blockchains develop. The two real-world case studies contribute to the information technology governance literature on blockchain governance. Originality/value The results of this case studies will be beneficial for developing theories and empirical models to determine antecedents for achieving consensus and trust in blockchain and testing the relationship between these factors and blockchain governance at different levels. As a result, theories related to the governance of blockchain technologies could be further developed.
A Vehicular Trust Blockchain Framework with Scalable Byzantine Consensus

The maturing blockchain technology has gradually promoted decentralized data storage from cryptocurrencies to other applications, such as trust management, resulting in new challenges based on specific scenarios. Taking the mobile trust blockchain within a vehicular network as an example, many users require the system to process massive traffic information for accurate trust assessment, preserve data reliably, and respond quickly. While existing vehicular blockchain systems ensure immutability, transparency, and traceability, they are limited in terms of scalability, performance, and security. To address these issues, this paper proposes a novel decentralized vehicle trust management solution and a well-matched blockchain framework that provides both security and performance. The paper primarily addresses two issues: i) To provide accurate trust evaluation, the trust model adopts a decentralized and peer-review-based trust computation method secured by trusted execution environments (TEEs). ii) To ensure reliable trust management, a multi-shard blockchain framework is developed with a novel hierarchical Byzantine consensus protocol, improving efficiency and security while providing high scalability and performance. The proposed scheme combines the decentralized trust model with a multi-shard blockchain, preserving trust information through a hierarchical consensus protocol. Finally, real-world experiments are conducted by developing a testbed deployed on both local and cloud servers for performance measurements.
Topgun: An ECC Accelerator for Private Set Intersection

Elliptic Curve Cryptography (ECC), one of the most widely used asymmetric cryptographic algorithms, has been deployed in Transport Layer Security (TLS) protocol, blockchain, secure multiparty computation, and so on. As one of the most secure ECC curves, Curve25519 is employed by some secure protocols, such as TLS 1.3 and Diffie-Hellman Private Set Intersection (DH-PSI) protocol. High-performance implementation of ECC is required, especially for the DH-PSI protocol used in privacy-preserving platform. Point multiplication, the chief cryptographic primitive in ECC, is computationally expensive. To improve the performance of DH-PSI protocol, we propose Topgun, a novel and high-performance hardware architecture for point multiplication over Curve25519. The proposed architecture features a pipelined Finite-field Arithmetic Unit and a simple and highly efficient instruction set architecture. Compared to the best existing work on Xilinx Zynq 7000 series FPGA, our implementation with one Processing Element can achieve 3.14× speedup on the same device. To the best of our knowledge, our implementation appears to be the fastest among the state-of-the-art works. We also have implemented our architecture consisting of 4 Compute Groups, each with 16 PEs, on an Intel Agilex AGF027 FPGA. The measured performance of 4.48 Mops/s is achieved at the cost of 86 Watts power, which is the record-setting performance for point multiplication over Curve25519 on FPGAs.
Nonlinear self-calibration method based on phase registration in phase-shifting profilometry

In phase-shifting profilometry, the nonlinearity of the projector is the main error source which undermines the 3D reconstruct accuracy. Most calibration algorithms mainly focus on calibrating the nonlinear responses and compensating for the related errors. However, it requires a tedious pre-calibration process. In this paper, a phase registration algorithm based on Gray code guidance is proposed, Hough transform and majority voting are performed to accurately locate the boundary of Gray code, two strategies are introduced to select correct phase matching pairs. In addition, to eliminate mismatched points and improve the accuracy of the fitting, outlier detection technology is used to iteratively fit phase matching pairs. Finally, the error is compensated point by point using the fitting curve. Experimental and simulation results demonstrate the effectiveness of the proposed method. It has the advantages of simple deployment, no photometric pre-calibration, high accuracy, strong robustness, and can handle the projector's nonlinearity that changes with time.
Shape recovery from fusion of polarization binocular vision and shading

The shape from polarization can recover the fine texture of the target surface. However, the gradient field for shape recovery by polarization is ambiguous, which is caused by the multi-value of the azimuth angle. In response to the problem, a method of correcting the ambiguity by the fusion of polarization binocular vision and shading information is proposed in this paper. An iterative optimization algorithm is designed to estimate the direction of the light source, which provides the basis for the shading method to calculate the depth map. Additionally. the low-frequency depth map generated by binocular matching is used to correct the polarization gradient field. The polarization gradient field of the holes and small zenith angle regions in the binocular are corrected by the improved shading method. In the experiment, four different material target objects were used for shape recovery and compared with other methods. The results of the fusion method proposed are better in terms of fine texture. At the camera working distance of 700 mm, the resolving power performs well and demonstrates that changes in the depth of at least 0.1 mm can be distinguished from that recovery result.
Analysis on Medication Rules of Four Classic Chinese Herbal Medicines in Dizziness Treatment Based on Data Mining

Based on the Traditional Chinese Medicine Inheritance Assistance Platform (V2.5), this study explores the medication patterns of famous traditional Chinese medicine practitioners using four classic herbal medicines to treat dizziness, providing medication reference for clinical treatment of dizziness.Collect prescriptions from CNKI for the treatment of dizziness using four classic herbs from Chinese traditional Chinese medicine practitioners, including 47 prescriptions for rhubarb, 74 prescriptions for Aconite root, 46 prescriptions for ginseng, and 122 prescriptions for prepared Radix Rehmanniae, and input them into the traditional Chinese medicine inheritance assistance platform to analyze their medication patterns.The total frequency of using tonifying qi and blood medicine is the highest.The four qi are mainly composed of cold, warm, and calm.The five flavors are sweet, bitter, and spicy.It mainly belongs to the liver, spleen, and kidney meridians, and is connected to the five organs.The most commonly used drug combination in prescriptions containing rhubarb is rhubarb and Gastrodia elata.The most commonly used drug combination for prescriptions containing Aconite root is Aconite root and Atractylodes macrocephala.The most commonly used drug combination for prescriptions containing ginseng is ginseng andAstragalus membranaceus; The most commonly used drug combination in prescriptions containing Radix Rehmanniae is Radix Rehmanniae and Cornus officinalis.Proficient in the medication methods of the four classic herbs, the treatment of dizziness has a significant therapeutic effect, reflecting that in deficiency syndrome, the main focus is on tonifying qi and blood, while in empirical syndrome, the basic treatment principles of wind, fire, phlegm, and blood stasis need to be distinguished.Famous traditional Chinese medicine practitioners have rich experience and are worth learning from.
Prediction and Classification Model Based on Wordle's Date

Wordle is an interesting puzzle that gained huge popularity in early 2022.Studying the game's play data is important for its development and promotion.That's why our team has conducted research in this area.Firstly, we used Prophet Model to explain the reasons for the change in the number of reports and to predict the interval of change in the number of reports on a certain day in the future.Then, based on the word attributes that were tested by the Apriori Model, we use Spectral Clustering Model to classify the word by difficulty.With the above model, we obtained the results with good reliability and interpretability.
The Development, Challenges and Suggestions of Digital RMB and RMB Internationalization

Digital economy is a new economic form with the development of big data, artificial intelligence and blockchain technology.The rapid development of digital economy has profoundly changed our way of production and life, and has great significance in the reconstruction of global factors and economic development.Driven by the digital economy, the academic circles at home and abroad have aroused a research boom.The People's Bank of China is also paying close attention to the impact of RMB digitalization on economic stability, RMB system and financial policies.With the development of the digital economy, the wave of many cryptocurrencies has been opened.In the context of cryptocurrencies, the development of digital currencies has been the general trend of the central bank, but also faced with severe challenges.As the country promotes the internationalization of RMB, the investment and promotion of digital RMB, ensuring the security of digital RMB and establishing an effective regulatory system are of great significance to the development of China's digital economy and the promotion of RMB internationalization.
Data preprocessing impact on machine learning algorithm performance

Abstract The popularity of artificial intelligence applications is on the rise, and they are producing better outcomes in numerous fields of research. However, the effectiveness of these applications relies heavily on the quantity and quality of data used. While the volume of data available has increased significantly in recent years, this does not always lead to better results, as the information content of the data is also important. This study aims to evaluate a new data preprocessing technique called semi-pivoted QR (SPQR) approximation for machine learning. This technique is designed for approximating sparse matrices and acts as a feature selection algorithm. To the best of our knowledge, it has not been previously applied to data preprocessing in machine learning algorithms. The study aims to evaluate the impact of SPQR on the performance of an unsupervised clustering algorithm and compare its results to those obtained using principal component analysis (PCA) as the preprocessing algorithm. The evaluation is conducted on various publicly available datasets. The findings suggest that the SPQR algorithm can produce outcomes comparable to those achieved using PCA without altering the original dataset.
Towards the Identification of Experts in Informal Learning Portals at Scale

During the past decade, there has been growing interest among researchers in informal learning at scale, particularly in the area of expert finding. These platforms have played a fundamental role in facilitating informal learning at scale, by providing access to diverse expertise and knowledge resources that might not otherwise be available to learners. Based on the encountered gaps in expert identification in Question & Answer (Q&A) portals, we inspect the feasibility of identifying data science experts in Reddit using the activity behaviour of every user, including Natural Language Processing (NLP), crowdsourced and user features sets. We also examine the impact of using only expert and non-expert classes versus three classes additionally including the out-of-scope class. Our findings can be used for distinguishing different types of users in Reddit, creating a recommendation system, identifying unreliable users or social bots in the early stage and reducing their influence.
Detecting bots in social-networks using node and structural embeddings

Users on social networks such as Twitter interact with each other without much knowledge of the real-identity behind the accounts they interact with. This anonymity has created a perfect environment for bot accounts to influence the network by mimicking real-user behaviour. Although not all bot accounts have malicious intent, identifying bot accounts in general is an important and difficult task. In the literature there are three distinct types of feature sets one could use for building machine learning models for classifying bot accounts. These feature-sets are: user profile metadata, natural language features (NLP) extracted from user tweets and finally features extracted from the the underlying social network. Profile metadata and NLP features are typically explored in detail in the bot-detection literature. At the same time less attention has been given to the predictive power of features that can be extracted from the underlying network structure. To fill this gap we explore and compare two classes of embedding algorithms that can be used to take advantage of information that network structure provides. The first class are classical embedding techniques, which focus on learning proximity information. The second class are structural embedding algorithms, which capture the local structure of node neighbourhood. We show that features created using structural embeddings have higher predictive power when it comes to bot detection. This supports the hypothesis that the local social network formed around bot accounts on Twitter contains valuable information that can be used to identify bot accounts.
The evolution of service toward automated customer assistance: there is a difference

Purpose The purpose of this paper is to explore the distinction between human-delivered service and technology-based, automated customer assistance. Design/methodology/approach This is a conceptual paper. There is no methodology. Findings The concept of service is primarily delivered when a human helps another. When technology is infused into the process and becomes the major component of delivering the aid that is requested, the process is automated customer assistance. Thus, “self-service” is not service. It is automated customer assistance. Research limitations/implications The definition of service is refined to describe the process of a human helping another person. When technology is used to provide the needed aid, it is no longer a service. Instead, it is automated customer assistance. The implication is that researchers should closely examine how users assess and perceive the two separate approaches to providing the needed aid. Practical implications The definition of service is refined to describe the process of a human helping another person. When technology is used to provide the needed aid, it is no longer a service. Instead, it is automated customer assistance. Researchers should closely examine how users assess and perceive the two separate approaches. Industry professionals should be mindful of the distinction between the delivery of service, which requires staff, and the provisioning of technology to provide assistance, which requires little to no staff. Intentionality should drive when customers are better helped by a human or by technology. Originality/value The value provided helps both providers create and users express when human-based service is needed versus assistance provided by technology.
Machine-Learning-Based Classification of Frequency Hopping in Radio Networks for Communication Reconnaissance

Abstract This article presents a customized approach for training a supervised learning neural network with the adaptive moment estimation algorithm, to classify the number of frequency hopping networks in an operational area. The algorithm was constructed based on data experimentally collected from a real-time spectrum analyzer for military very high frequency hopping networks. The impact of some training parameters on classification efficiency is briefly discussed while the obtained accuracy was above 97% for both test and validation data in all training variations. With this promising result, the proposed algorithm has the potential to be utilized in developing operational systems capable of real-time signal reconnaissance for military frequency hopping radio networks.
Improved Security for Multimedia Data Visualization Using Hierarchical Clustering Algorithm

In this paper, a realization technique is designed with unique analytical model for transmitting multimedia data to appropriate end users. Transmission of multimedia data to all end users through a variety of visualization methods is the foundation of future computer systems. Yet, highly limited system resources prevent the updating of the methods used to manage multimedia data. Hence, a high-end visualization technique where uncertainties are eliminated is required for the visualization process with a multimedia system. As a result, the suggested system incorporates a clustering technique utilizing an analytical framework to ensure a high degree of transmission for all multimedia data. The technical contribution of the proposed method depend on multimedia visualization process that takes place with high security features by including necessary parametric relationships such as occurrence of jitter, data density points, time period, multimedia storage, data smoothness and distance. For the established parametric relationship the validation methodology is integrated with hierarchical clustering algorithm thereby transmitting every clustered data with high security feature thereby the examined outcomes under five scenarios proves that data security which is represented by simulation outcomes is improved to 88% as compared to existing approach.
General Results of Assessment of Supraprofessional Competencies of 1st‑4th Year Students

Subject . The problem of developing “soft” skills of recent graduates is acute in some areas of business. According to employers, young employees may have a high level of knowledge, but a low level of soft skills. The article considers the primary results of the pilot year of the project “Assessment and development of managerial competencies in Russian universities” (“Competence Centers”). The following competencies were assessed: information analysis; partnership and cooperation; planning and organization; result orientation; stress tolerance; following rules. Goals . Describe cross-professional competencies among university students; reveal links between different soft and hard competencies in university students. Methodology . The sample consisted of 28,933 students from 51 Russian universities. The following instruments were used to assess students’ soft skills: The Universal Personality Questionnaire, The Resilience Questionnaire, Tests for the analysis of numerical and verbal information. The analysis included correlational analysis, t-test and descriptive statistics. Results . The competencies “Stress resistance” and “Result orientation”, “Planning” and “Stress resistance” are strongly positively related (correlations are from 0.75). The competencies “Result orientation” and “Following the rules”, “Stress resistance” and “Following the rules” are negatively related (correlations are from — 0.22). The Information Analysis competency is not related to other competencies. The 4th year students show a higher result in “Information Analysis” and “Following the Rules” than the 1st year students. Conclusions . We describe differences in cross-professional competencies among students of different years of study and discuss possible explanations for these differences.