Reviewerly/exHarmony huggingface dataset:
Comparative Analysis of Public Transportation Development in Developing and Developed Countries

This study to analyze the development of Public Transportation in the application of smart cities in the United States, China, Italy, and Brazil. The result of public transportation services dramatically contributes to increase mobility, and safety, as well as having a positive impact in urban areas. This research method uses a qualitative approach to bibliometric analysis. The data source is taken from the Scopus and articles searched for the last ten years (2012-2021) using keywords smart city and public transportation. The data found, 293 articles, with the top four countries having the highest number of articles. United States by 41, China by 32, Italy by 23, and Brazil by 20. The data analysis technique in this study used the Nvivo12plus and Vosviwer. The study results there are three concept findings in the development of public transportation; mobility, policy, and connectivity. The four countries have different focuses on implementing public transportation. United States focuses on mobility, policy, and connectivity with indicators such as urban mobility, intelligent transportation management, and governance. China focuses on mobility and connectivity, smart urban mobility, criteria, infrastructure, economy, and the environment, China is not the policy concept. Italy and Brazil also focus on the idea of mobility and policy with indicators that focus on the progress of urban mobility analysis and efforts to improve the convenience of public transportation to increase demand. The concept of connectivity has not been a concern for Italy and Brazil. Within these four countries, the United States dominates the development of public transportation, because it has fulfilled the three concepts of public transportation, namely mobility, policy, and connectivity. It is hoped that from the analysis of the development of this research, the four countries can further improve technological advances based on the Intelligent Transport System (ITS) to make public transportation smarter.
Resource provisioning optimisation for cloud computing systems serving multi-class requests

With many organisations switching to cloud-based systems, the variety of domains using cloud services is increasing steadily. Often, companies own private cloud computing systems to cater to their own needs and reduce dependency on third-party services. These systems serve a multitude of requests of different classes that often require different types of resources. A queuing network-based cost model has been employed to quantify the economics of the system, and an optimisation problem has been set up. In the cost function, higher-order terms have been used which accounts for diminishing marginal productivity. This law is used in microeconomics to study the behaviour of cost for individual service-producing entities, i.e., firms. In this study, the cost of the system has been optimised by calculating the optimal quantity of resources provisioned to the cloud system and the behaviour of the cost of maintaining a profitable cloud-computing system has been discussed.
Virtual Excitations and Quantum Correlations in Ultra-Strongly Coupled Harmonicoscillators Under Intrinsic Decoherence

We study the intrinsic decoherence of coupled harmonic oscillators. The Milburn master equation is solved exactly, and the dynamics of virtual ground state excitations are investigated. The interaction of quantum correlations and virtual excitation was then studied. The following is a summary of our major findings. (i) The damped oscillatory profile of all three quantities is the same. (ii) Ultra-strong coupling combined with huge anisotropy values results in the reemergence of entanglement and steering. (iii) To sustain entanglement and steering, virtual excitations are required. (iv) The quantum correlations are amplified in the quantum synchronous regime. (v) Ultra-strong couplings cause inherent decoherence to be avoided.
Analysis of Featute Extraction Techniques for Medical Images

In the image downloading process, image processing method, data mining method, and computer scanning technique, feature removal is an important step. The process of extracting logical data from original data is known as feature extraction. However, many FE methods still struggle with the difficulty of extracting relevant features that can accurately capture the basic content of a piece of data or database. We provide a survey of existing methods of extracting features used in recent years in this work. Brightness, homogeneity, entropy, meaning, and strength were shown to be the most of the distinctive features that could be obtained when using global learning and development community features extraction method in the images in the study. In addition, it was found that the extraction methods are not specific to the application and can be used in a variety of situations.
Design and Performance Evaluation of Dram Storage Arrays with Different Technologies

1T1C DRAM cell has been simulated using Tanner EDA tool over 180nm technology and array of 2*2 DRAM, 4*4 DRAM has also been simulated over 180nm.H-spice tool is also used to simulate 1KB DRAM memory over 22nm technology using two capacitors to reduce the leakage as well as 1T1C DRAM cell.Power consumption and delay are the important parameters to design any circuits.Hence, they are also computed with the retention time.Timing parameters such as row address to column address delay, row pre-charge time, latency time have also computed.
Topic-Level Taxonomy Generation

In the first part of the book, we present algorithms for cube construction. They allow for organizing unstructured text data into a multidimensional, multi-granular cube structure, such that users can explore and retrieve relevant data with declarative queries easily. Recall Figure 1.3, in the entire corpus are allocated into one cell of the cube structure, thereby enabling users to select task-relevant data for on-demand analysis. The cube construction process mainly involves two subtasks: (1) taxonomy generation—how does one discover the taxonomic structure for each dimension? and (2) document allocation—how does one allocate all the documents into the cube by choosing the most appropriate label in each dimension? Answering these two questions is easy when the desired dimensions are explicitly specified in meta-data. However, when the dimensions are implicitly hidden in the unstructured text and need to be inferred, these two tasks become nontrivial. In the following two chapters, we will describe methods for the taxonomy generation task with minimal supervision. Given a collection of concept terms (e.g., entities, noun phrases) related to a cube dimension, taxonomy generation aims at organizing the given terms into a concept hierarchy that reflect the parent-child relationships among these concepts. As aforementioned, there are two types of taxonomies: topic-level and term-level. The former defines each node as a group of topically coherent terms; and the latter defines each node as a single term to represent a concept. We first present TaxoGen in this chapter for generating topic-level taxonomies, and then present HiExpan in next chapter for generating term-level taxonomies.
Self-supervised Learning with Deep Neural Networks for Computer Vision

Self-supervised learning has gained popularity in recent years due to a need to avoid the expensive costs of large-scale data annotation. This field has had huge developments in recent months, with the state-of-the-art self-supervised learning methods achieving results that surpass their supervised counterparts on the ImageNet dataset. In this study, we investigate and implement 2 forms of self-supervised learning: Momentum Contrast (MoCo) and autoencoders, which are contrastive and generative methods, respectively. Through several experiments, we analyze the quality of the latent representations that are learnt by each method and assess whether they allow for increased performance when labels are scarce. We also propose a dual head network for self-supervised learning, combining elements from both the above methods and study the effectiveness of such a method.
Ideals in BE-algebras Based on Lukasiewicz Fuzzy Set

For the purpose of applying the concept of Lukasiewicz fuzzy set to ideals in BE-algebras, Lukasiewicz fuzzy ideal is introduced, and its properties are studied. The relationship between fuzzy ideal and Lukasiewicz fuzzy ideal is discussed. Conditions for the Lukasiewicz fuzzy set to be a Lukasiewicz fuzzy ideal are provided, and characterizations of Lukasiewicz fuzzy ideal are displayed. Conditions in which three subsets, called ∈-set, q-set and O-set, are ideals are explored.
Lukasiewicz Fuzzy BE-algebras and BE-filters

By applying the concept of Lukasiewicz fuzzy set to BE-algebras, the notions of Lukasiewicz fuzzy BE-algebra and Lukasiewicz fuzzy BE-filter are introduced, and their properties are investigated. Characterizations of Lukasiewicz fuzzy BE-algebra and Lukasiewicz fuzzy BE-filter are discussed, and the relationship between fuzzy BE-algebra (resp., fuzzy BE-filter) and Lukasiewicz fuzzy BE-algebra (resp., Lukasiewicz fuzzy BE-filter) is established. The conditions for the ∈-set, q-set and O-set of Lukasiewicz fuzzy set to be BE-subalgebras are explored. Lukasiewicz fuzzy BE-filter is created by using BE-filter.
Characterization of Different User Behaviors for Demand Response in Data Centers

Digital technologies are becoming ubiquitous while their impact increases. A growing part of this impact happens far away from the end users, in networks or data centers, contributing to a rebound effect. A solution for a more responsible use is therefore to involve the user. As a first step in this quest, this work considers the users of a data center and characterizes their contribution to curtail the computing load for a short period of time by solely changing their job submission behavior. The contributions are: (i) an open-source plugin for the simulator Batsim to simulate users based on real data; (ii) the exploration of four types of user behaviors to curtail the load during a time window, namely delaying, degrading, reconfiguring or renouncing their job submissions. We study the impact of these behaviors on four different metrics: the energy consumed during and after the time window, the mean waiting time and the mean slowdown. We also characterize the conditions under which the involvement of users is the most beneficial.
Study on the Blending of College Chinese Teaching and Traditional Culture Education

With the deepening of college Chinese teaching, Chinese in colleges has become an important carrier of traditional culture education, playing a significant role in traditional culture education.However, the present condition of combination between college Chinese with traditional culture education is not optimistic.Therefore, in order to enrich related study, this paper focuses on the penetration of deep national culture.
Predicting traffic induced noise using artificial neural network and multiple linear regression approach

Many countries have implemented noise regulations by enacting emission guidelines and regulating building acoustics.Unlike many other environmental issues, noise pollution continues to worsen, accompanied by an increase in the number of complaints from those who are affected.Most people are exposed to a variety of noise sources, with road traffic noise being the most common.The traffic management work is typically carried out by traffic
LA COMPETENCIA DIGITAL EN LA ENSEÑANZA MEDIADA POR TECNOLOGÍA UNA PRIMERA REFLEXIÓN POSPANDEMIA

Digital competence in technology-mediated learning. A first post-pandemic reflectionThis article provides a mapping of ICT inclusion education policies and digital competency frameworks and the relevance of understanding their scope in a pandemic and post-pandemic context.Firstly, we review how the 1-to-1 models of digital inclusion have burst into education systems and how they reflect a view of the incorporation of ICT from a perspective that takes into account the student and the development of digital competence (DC) in them, to then delve into the development of digital competence of teachers (TDC) as a relevant actor in teaching and learning processes mediated by digital technologies.Finally, it reflects on research results in relation to the tools that education in Uruguay has had to face an accelerated transition towards an emergency remote teaching, as the only educational response in a context of uncertainty in pandemic and how it could be thought post-pandemic and for whom in a continuum from face-to-face mediated by digital technologies to full virtuality.
A Knowledge Query Network Model Based on Rasch Model Embedding for Personalized Online Learning

The vigorous development of online education has produced massive amounts of education data. How to mine and analyze education big data has become an urgent problem in the field of education and big data knowledge engineering. As for the dynamic learning data, knowledge tracing aims to track learners' knowledge status over time by analyzing the learners' exercise data, so as to predict their performance in the next time step. Deep learning knowledge tracking performs well, but they mainly model the knowledge components while ignoring the personalized information of questions and learners, and provide limited interpretability in the interaction between learners' knowledge status and questions. A context-aware attentive knowledge query network (CAKQN) model is proposed in this paper, which combines flexible neural network models with interpretable model components inspired by psychometric theory. We use the Rasch model to regularize the embedding of questions and learners' interaction tuples, and obtain personalized representations from them. In addition, the long-term short-term memory network and monotonic attention mechanism are used to mine the contextual information of learner interaction sequences and question sequences. It can not only retain the ability to model sequences, but also use the monotonic attention mechanism with exponential decay term to extract the hidden forgetting behavior and other characteristics of learners in the learning process. Finally, the vector dot product is used to simulate the interaction between the learners' knowledge state and questions to improve the interpretability. A series of experimental results on 4 real-world online learning datasets show that CAKQN has the best performance, and its AUC value is improved by an average of 2.945% compared with the existing optimal model. Furthermore, the CAKQN proposed in this paper can not only track learners' knowledge status like other models but also model learners' forgetting behavior. In the future, our research will have high application value in the realization of personalized learning strategies, teaching interventions, and resource recommendations for intelligent online education platforms.
BUSINESS PROCESSES OF EDUCATIONAL SERVICES IN THE CONTEXT OF ECONOMIC MODERNIZATION AND NEW CHALLENGES

The purpose of the article is to reveal the concept of process approach to improve the quality of educational services of Higher Education System on the basis of highlighting the features of management of Higher Education institutions and the market of educational services in modern economic conditions. The article proves that currently the educational sphere is being formed very actively. It is stressed that the demands of society on the quality of education are increasing, learning technologies are constantly changing and being updated, innovations are emerging, and competitive rivalry between educational organizations is intensifying. The authors draw attention to the fact that it leads to the search for new effective ways to increase the competitiveness of educational organizations. The competitiveness of educational services is manifested in the orientation of educational organizations to the future state of the labor market. Much attention is given to business processes of educational services of higher educational institutions. The theoretical and methodological basis of the research are the works of Russian and foreign scientists devoted to the development of management of the higher education system, as well as the application of a process approach to management decisions aimed at improving the quality of educational services. As a result, the authors determine the role of the Higher Education System in the formation and development of human capital and modern challenges for Higher Education institutions. The expediency of applying a process approach to the management of educational services is reasoned. The key principles for the allocation of business processes of educational services and organizational and managerial measures to optimize them and improve the efficiency of functioning are proposed.
Using Fuzzy Neural Network to Assess Network Video Quality

At present people have higher and higher requirements for network video quality, but video quality will be impaired by various factors, so video quality assessment has become more and more important.This paper focuses on the video quality assessment method using different fuzzy neural networks.Firstly, the main factors that impair the video quality are introduced, such as unit time jamming times, average pause time, blur degree and block effect.Secondly, two fuzzy neural network models are used to build the objective assessment method.By adjusting the network structure to optimize the assessment model, the objective assessment value of video quality is obtained.Meanwhile the advantages and disadvantages of the two models are analysed.Lastly, the proposed method is compared with many recent related assessment methods.This paper will give the experimental results and the detail of assessment process.
Combinatorial secure codes for copyright protection and related problems

The rapid development of modern science and technology not only provides convenience for data communication, but also poses a tremendous threat to the copyright of digital content. This paper focuses on the mathematical theory of traitor-tracing for copyright protection and its latest progress. First, for applications in different scenarios, such as broadcast encryption and multimedia fingerprinting, weintroduce unified models to characterize the (key/fingerprint) distribution schemes with traceability property and frameproof property, respectively. Next, we review several classes of combinatorial secure codes with the traceability property and frameproof property, and the combinatorial methods used to investigate the bounds on the maximum code size and the explicit constructions, as well as the latest results and the open problems. The relationships between the combinatorial problems in copyright protection and the related problems in group testing and multiple access communication are discussed as well.
Adaptive learning model for prin ted circuit boards (pcb) module designing in an intelligent learning system

The prospects for the adaptation usage in an intelligent learning system for designing modules based on printed circuit boards (PCB) are considered. Models of primary adaptation, training scenario, trained designer, adaptation of training materials, subject area, testing and practical tasks are created. The use of models in an adaptive learning system allows you to build a learning process aimed at consistent and unconditional learning of educational material for different levels of students. The practical significance of the work lies in the program for the implementation of the proposed models of adaptive control of the learning process according to the individual characteristics of the student in the development of an intelligent adaptive learning system. The results of the study have found practical application at the stages of the project development cycle at the Vietnamese shipyard X52 to increase the level of engineer competence in the design of modules based on PCB. Keywords adaptation, adaptive learning, intelligent learning system, module design, printed circuit board (PCB)
Automated Face Authentication and Recognition Using Deep Neural Network with SVM Classifier in Cloud Environment

With the development of technology improvement, crime rates are also getting increased nowadays. The number of cameras is installed everywhere, making criminal tracking simpler. The fast-growing demand of surveillance cameras is creating the necessity of real-time analytics, mining, and the need of public clouds for private sectors since cost and scalability are the major issue. With the aim to providing promising solution to the security threat, this research work is proposed to easily identify, recognize, and track the criminals on demand, missing children, etc. In order to meet this requirement, a dynamic neural network approach with classifier SVM is proposed. A sophisticated platform is required for image processing as it is awfully expensive in the context of time needed for computational tasks and storage space. Also, it is very important to acquire economic solutions. The requirement of adopting economic solutions and replacement of traditional systems, that is the large-scale data processing requirements, made us to use cloud computing since it is capable of providing highly required services with wide availability and extensibility. With this proposed model, the minimum error rate of 0.04 is achieved when tested with 1000 data samples.
An Optimized K-means Clustering Approach on Top of MapReduce

Cluster analysis had always been a hot research and application topic, since it enables the extraction of meaningful patterns and relationships from large (structured and unstructured) datasets. K-means is one of the most popular cluster analysis techniques which has been widely adopted to solve many critical problems due to its simplicity and time efficiency compared to other clustering techniques. Nonetheless, K-means suffers from several drawbacks that stem mainly from its random selection of the initial centroids (i.e., cluster centers) and its reliance on local optimal partitions. In this paper, we provide two contributions toward boosting the performance and efficiency of K-means in big data environments. In the first contribution, we integrate a statistical method on top of K-means to optimize the process of selecting initial centroids, which helps improve the final clustering structure and reduce the time to converge to a stable clustering structure. In the second contribution, we illustrate how K-means can be practically implemented using the MapReduce programming model and provide the details of the map and reduce functions. The objective is to help researchers and practitioners apply K-means on huge datasets using the state-of-the-art big data analytics platforms such as Hadoop and Apache Spark.