Reviewerly/exHarmony huggingface dataset:
Cosine Similarity – A Computing Approach to Match Similarity Between Higher Education Programs and Job Market Demands Based on Maximum Number of Common Words

Comparing textual content is becoming more and more problematic due to the fact that nowadays data is very dynamic. The application of sophisticated methods enables us to compare how similar the documents are to each other. In our research we apply the Cosine Similarity method to compare the similarity of several documents with each other. We also apply the TF-IDF technique which enables us to normalize the results. Normalization of these results is necessary for the fact that there are some words that are repeated several times and from this repetition is determined their importance. Finally we can see a comparison between the similarity of documents with normalized and non-normalized results. As can be seen in the research, the normalization of results has a great value in comparing documents with textual content.
THE READINESS OF PUBLIC UNIVERSITIES IN ADOPTING INDUSTRIAL REVOLUTION 4.0 (IR 4.0) FROM A CONSTRUCTION MANAGEMENT PERSPECTIVE

There have been four major Industrial Revolutions that have developed human lifestyles. Currently, the Fourth Industrial Revolution, (IR 4.0), is taking place in major industries with current students and graduates requiring new talents and skills to become a future-proof workforce. However, current tertiary education is not yet fully equipped to deal with IR4.0 concepts. This results in the low awareness of IR4.0 among students and graduates where they are not yet ready to enter an IR4.0 workspace. This is especially so in construction management education. Hence, the aim of this research is to improve existing construction engineering education by including IR4.0. This research is based on data collected from a survey by questionnaire responses from construction management undergraduate and postgraduate students at Universiti Teknologi MARA (UiTM) Shah Alam. The findings show that these construction management students agreed that challenges such as the traditional construction management curricular structure and the lack of IR4.0 facilities hindered the implementation of IR4.0 in construction management education. Therefore, recommendations such as the collaboration of industry experts with universities and students to enable the adoption of IR4.0 into project-based learning were discussed. In conclusion, this study hopes to help both universities and students to be prepared for the integration of and benefits from, implementing IR4.0 in construction management education.
BLOCKCHAIN-BASED DECENTRALIZED USER AUTHENTICATION SCHEME FOR LETTER OF GUARANTEE IN FINANCIAL CONTRACT MANAGEMENT

The use of blockchain technology in the financial contract management system leads many challenges for user authentication and key distribution. In traditional financial system, the core endeavor with strong letter of guarantee play an irreplaceable role in the supply chain management. In this context, fraud in financial contract management is severe problem for economical growth. To overcome the problems of the conventional transaction process, in this paper, we develop a user access control and key management scheme that uses blockchain decentralized network to manage the letter of guarantee. This decentralized network platform can helps the problem of no trust among the users, improves the efficiency of data transmission, reduces costs, and provides better financial services to the relevant parties in the supply chain. The proposed user authentication is developed based on the deterministic encryption algorithm to achieve decentralized security for trusted data transmission. Finally, the experimental results shows that the computation cost of the proposed authentication increases due to the rising of number of customer added to the blockchain network. Overall the proposed authentication scheme most suitable for banking system to issue the LoG contract in right time.
An alternative cloud index for estimating downwelling surface solar irradiance from various satellite imagers in the framework of a Heliosat-V method

Abstract. We develop a new way of retrieving the cloud index from a large variety of satellite instruments sensitive to reflected solar radiation, embedded on geostationary and non-geostationary platforms. The cloud index is a widely used proxy for the effective cloud transmissivity, also called the “clear-sky index”. This study is in the framework of the development of the Heliosat-V method for estimating downwelling solar irradiance at the surface of the Earth (DSSI) from satellite imagery. To reach its versatility, the method uses simulations from a fast radiative transfer model to estimate overcast (cloudy) and clear-sky (cloud-free) satellite scenes of the Earth’s reflectances. Simulations consider the anisotropy of the reflectances caused by both surface and atmosphere and are adapted to the spectral sensitivity of the sensor. The anisotropy of ground reflectances is described by a bidirectional reflectance distribution function model and external satellite-derived data. An implementation of the method is applied to the visible imagery from a Meteosat Second Generation satellite, for 11 locations where high-quality in situ measurements of DSSI are available from the Baseline Surface Radiation Network. For 15 min means of DSSI, results from our preliminary implementation of Heliosat-V and ground-based measurements show a bias of 20 W m−2, a root-mean-square difference of 93 W m−2, and a correlation coefficient of 0.948. The statistics, except for the bias, are similar to operational and corrected satellite-based data products HelioClim3 version 5 and the CAMS Radiation Service.
Classification of MNIST Image Dataset Using Improved Convolutional Neural Network

Abstract— Convolutional Neural Network (CNN) holds the current research interest in the ever-evolving image classification field. Accurate classifying the image data with minimum of time is highly desired. But the traditional CNN architecture often fails to generate the appropriate outcome for large dataset. So, a modified approach of CNN is proposed here which is the combination of data augmentation and batch normalization embedded with CNN. Now a days identifying or classifying digits accurately with variety of modes is really a task of challenge. The advantages of the proposed approach are noted when it is applied to the popular MNIST dataset used for digit classification. The proposed approach has been compared with some existing techniques and results infer that the validation, training loss and testing accuracy of the proposed approach are more superior as compared to the state-of-art approaches.
Neural Network Prediction Model to Explore Complex Nonlinear Behavior in Dynamic Biological Network

Organism network systems provide a biological data with high complex level. Besides, these data reflect the complex activities in organisms that identifies nonlinear behavior as well. Hence, mathematical modelling methods such as Ordinary Differential Equations model (ODE's) are becoming significant tools to predict, and expose implied knowledge and data. Unfortunately, the aforementioned approaches face some of cons such as the scarcity and the vagueness in the biological knowledge to expect the protein concentrations measurements. So, the main object of this research presents a computational model such as a neural Feed Forward Network model using Back Propagation algorithm to engage with imprecise and missing biological knowledge to provide more insight about biological systems in organisms. Therefore, the model predicts protein concentration and illustrates the nonlinear behavior for the biological dynamic behavior in precise form. Also, the desired results are matched with recent ODE's model and it provides precise results in simpler form than ODEs.
Computing Method for Binomial Expansions and Geometric Series

This paper presents a computing method for the sum of summation of geometric series and the summation of series of binomial expansions in an innovative way. Geometric Series plays a vital role in the field of combinatorics including binomial coefficients. The multiple summations of series of binomial coefficients or computation of multiple binomial expansions are equal to the exponents of two. These methodological advances are useful for the researchers who are working in science, engineering, economics, computation, and management.
Fuzzy Medical Computer Vision Image Restoration and Visual Application

In order to shorten the image registration time and improve the imaging quality, this paper proposes a fuzzy medical computer vision image information recovery algorithm based on the fuzzy sparse representation algorithm. Firstly, by constructing a computer vision image acquisition model, the visual feature quantity of the fuzzy medical computer vision image is extracted, and the feature registration design of the fuzzy medical computer vision image is carried out by using the 3D visual reconstruction technology. Then, by establishing a multidimensional histogram structure model, the wavelet multidimensional scale feature detection method is used to achieve grayscale feature extraction of fuzzy medical computer vision images. Finally, the fuzzy sparse representation algorithm is used to automatically optimize the fuzzy medical computer vision images. The experimental results show that the proposed method has a short image information registration time, less than 10 ms, and has a high peak PSNR. When the number of pixels is 700, its peak PSNR can reach 83.5 dB, which is suitable for computer image restoration.
Integration of Public Transportation in Smart Transportation System (Smart Transportation System) in Jakarta

Congestion is not a new problem for Jakarta, as the center of government and a metropolitan city in Indonesia. Handling the congestion problem in Jakarta then requires a comprehensive and comprehensive solution. Smart cities and smart transportation systems are thought to be able to do just that. This paper aims to review the existing smart transportation system in Jakarta and attempt to integrate public transportation into this system as an effort to overcome congestion in Jakarta. The review in this paper is carried out qualitatively by reviewing various literatures that have been published previously. The results show that the government has started trying to develop a smart city through the Jakarta Smart City (JSC) so that the development of a smart transportation system as part of a smart city is needed. In this case, this paper has developed a framework for a smart transportation system that integrates the public transportation system into it so that a more integrated smart transportation system is created for Jakarta.
Lightweight Hybrid CNN-ELM Model for Multi-building and Multi-floor Classification

Machine learning models have become an essential tool in current indoor positioning solutions, given their high capa-bilities to extract meaningful information from the environment. Convolutional neural networks (CNNs) are one of the most used neural networks (NNs) due to that they are capable of learning complex patterns from the input data. Another model used in indoor positioning solutions is the Extreme Learning Machine (ELM), which provides an acceptable generalization performance as well as a fast speed of learning. In this paper, we offer a lightweight combination of CNN and ELM, which provides a quick and accurate classification of building and floor, suitable for power and resource-constrained devices. As a result, the proposed model is 58% faster than the benchmark, with a slight improvement in the classification accuracy (by less than 1 %).
Online Learning Through LMSs: Comparative Assessment of Canvas and Moodle

Learning Management Systems have become one of the primary means of organizing online learning and teaching processes. They provide educators with tools to create, host, and manage various materials for learners. The variety of modern LMSs is immense and requires profound research for making the right choice. The article is aimed at examining several aspects of online learning provision. Firstly, the study has surveyed world educators to identify what LMSs are more frequently applied in their professional practice. Secondly, based on the data obtained, the article examines two LMSs, Canvas and Moodle, in terms of their efficiency and ease of use for educators and students. The research presents a comprehensive comparison of four sets of features, which respondents emphasized during the survey. They include User Interfaces and the enrollment process, the efficiency of these two systems for students and teachers, integration with other platforms and programs, a set of attributes in terms of functionality, and opportunities for comprehensive assessments and grading. Thus, the purpose of the study is to conduct a comprehensive comparison of two LMSs, Moodle and Canvas, defined as one of the most required and topical by world educators and students, and reveal their possibilities for the implementation in the educational process of various establishments and students of different age.
Comparative Performance of Ensemble Machine Learning for Arabic Cyberbullying and Offensive Language Detection

Abstract In recent years, research on abusive language and cyberbullying detection have gained a great deal of interest since it affects both individual victims and societies. Hateful communications, bullying, sexism, racism, aggressive content, harassment, toxic comments, and other forms of abuse have all increased dramatically due to the ease of access to social media platforms such as Facebook, Instagram, Twitter, and others. As a result, there is a significant need to identify, manage, and restrict the spread of offensive content on social networking sites, prompting us to perform this study to automate the detection of offensive language or cyberbullying. Having a balanced data set for a model would generate higher accuracy models, thus we build a new Arabic balanced data set to be used in the process of offensive detection. Lately, Ensemble Machine Learning has been used to enhance the performance of single classifiers. The aim of this study is to compare the performance of different single and ensemble machine learning algorithms in detecting Arabic text containing cyberbullying and offensive language. For this purpose, we have chosen three machine learning classifiers and three ensemble models and apply them to three Arabic datasets two of them are offensive datasets that are publicly available, and the third one which we constructed. The results showed that the ensemble machine learning methodology outperforms the single learner machine learning approach. Voting performs is the best among the trained ensemble machine learning classifiers, with accuracy scores of (71.1%, 76.7%, and 98.5%) for the three used datasets respectively, exceeding the score obtained by the best single learner classifier (65.1%, 76.2%, and 98%) for the same datasets. Finally we use hyperparameters tunning on the Arabic cyberbylluing data set to optize the performance of the voting technique.
Ontologies as Building Blocks of Cloud Security

Much like other processing domains, cloud computing is not surely safe. Security of cloud processing needs the same attention as any other aspect of cloud processing requires. Cyber world is shifting toward ontological technique and web 3.0 or web semantics for security. Cloud hosts servers demand more attention in context of security as number of resources and their access increases. Security measures have to be more extensive in cloud. Semantic web is usually new revolution inside the web science, which usually works upon base of ontologies. Ontologies are receiving great attention in the domain of computing and hence in the domain of security. This review paper examines different proposed “ontology centered techniques” and also provides a comprehensive analysis on these tactics. This research paper gives critical analysis of different models presented by different authors and researchers for ensuring security of a cloud based environment. This analysis helps different vendors of cloud technology to adapt one or all of these models to practically implement in their cloud machines whether they are offering IaaS, PaaS or SaaS. Any new security model using ontologies can also be proposed based on this study, as this paper gives a comprehensive comparison of the previously proposed ontologies for monitoring security state of cloud environment as safe or malicious.
Development trends and challenges of students' academic mobility in higher education

The article presents the evaluation of students' international academic mobility in higher education in the context of the scientific literature, where the barriers to the development of academic mobility are singled out, the more effective management tools of academic mobility and their effectiveness are discussed.The aim of the research: after identifying the problematic areas of the implementation of international academic mobility in higher education, to anticipate the possibilities of its development.Analytical descriptive, quantitative and statistical research methods were applied.Using the quantitative research strategy, the experience of participation of Lithuanian higher education students (n = 349) in the Erasmus+ mobility programme was clarified, the reasons for non-participation in international academic mobility programmes were revealed and the factors determining higher student mobility indicators were singled out.The results of the study revealed the need to improve the system for the promotion and implementation of international academic mobility in higher education, as the results of the study showed that only a small number of research participants took part in the Erasmus+ programme.Despite the fact that participation in academic mobility programmes provides experience in communication and cooperation with representatives of other cultures, professional foreign language development, independence skills are developed, however, according to the research participants, fear, self-confidence due to insufficient professional knowledge and skills, lack of foreign language knowledge, intimidating selection procedures and interviews, and fears of possible difficulties in adapting when moving to another country, as well as reluctance to break up with family or close friends and insufficient financial support (scholarship), are among the reasons for not participating in Erasmus+ mobility.When assessing the possibilities of developing international academic mobility in higher education, the highest average scores show that higher scholarships and paid internships, as well as opportunities for groups of students to go to the same higher education institution or company in another country and places offered (educational institutions, internship places), the diversity of of places for academic exchanges and the high level of quality of professional training (education, studies and internships), according to the research participants, would encourage students to participate more actively in international academic mobility programmes.The results of the study will have practical implications for anticipating possible more effective tools for managing academic mobility in higher education.
Sortledton

Despite the wide adoption of graph processing across many different application domains, there is no underlying data structure that can serve a variety of graph workloads (analytics, traversals, and pattern matching) on dynamic graphs with transactional updates. In this paper, we present Sortledton, a universal graph data structure that addresses the open problem by being carefully optimizing for the most relevant data access patterns used by graph computation kernels. It can support millions of transactional updates per second, while providing competitive performance (1.22x on average) for the most common graph workloads to the best-known baseline for static graphs - CSR. With this, we improve the ingestion throughput over state-of-the-art dynamic graph data structures, while supporting a wider range of graph computations under transactional guarantees, with a much simpler design and significantly smaller memory footprint (2.1x that of CSR).
Robust 4-D Hyperchaotic DNA Framework for Medical Image Encryption

With the integration of cloud computing approaches in the healthcare systems, medical images are now processed and stored remotely on third-party servers. For such digital medical image data, privacy, protection, and security must be maintained by using image encryption methods. The aim of this paper is to design and apply a robust medical encryption framework to enhance the protection of medical image transformation and the patient information confidentiality. The proposed Framework encrypt the digital medical images using DNA computation and hyperchaotic RKF-45 random sequence approach. For which, the DNA computation is enhanced by applying hyperchaoticRKF-45 random key to the different Framework phases. The simulation results on different medical images were measured with various security analyses to prove the proposed framework randomness and coherent. Simulation results showed the ability of the hyperchaotic DNA encryption framework to withstand multiple electronic attacks with high performance compared to its counterparts of encryption algorithms. Finally, simulation and comparative studies have shown that, the proposed cryptography framework reported UACI and NPCR values 33.327 and 99.603 respectively, which are near to the theoretical optimal value.
Correction: Günlü, O. Function Computation under Privacy, Secrecy, Distortion, and Communication Constraints. Entropy 2022, 24, 110

Special case results given in Lemmas 1-4 and evaluated in Section 4 [...].
Spam Mail Detection using Machine Learning

Abstract: Spam email is one of the most serious problems in the online world. Nowadays, a large portion of the population relies on available emails or communications from strangers. As a result, the fact that anyone can leave an email or a message opens the door for spammers to compose spam messages concerning our various interests. Spam fills up our inbox with unnecessary messages, slowing down our internet connection and stealing valuable information such as our contact information and accurate information. Detecting spammers and spam content is a major issue of research and time-consuming tasks. Email spam is when someone sends out a large number of emails in a short period of time. The purpose of spam filtering is to determine whether an email is spam or ham. With this proposed system the specified mail can be detected as spam or ham and also IP address of mail.
Freedom of expression or censorship of antisemitic hate speech?

Taking the dilemma between freedom of expression and censorship of antisemitic hate speech as a point of departure, this article explores how three prominent and controversial Norwegian far-right alternative media perceive and perform comment moderation and how editorial and audience perspectives on the issue correspond. Based on a critical discourse analysis of interviews with key staff members and a strategic selection of comment sections, the article demonstrates how both moderators and debaters understand the boundaries between legitimate and illegitimate expressions and how transgressive content should be dealt with. The article argues that when it comes to regulating comment sections, these oppositional media actors are not so alternative after all. The study illustrates how comment moderation is crucial for all actors who seek to obtain or protect their legitimacy, regardless of their (counter-) position in the public sphere. While there is widespread agreement on antisemitic hate speech as illegitimate, there is, however, more tolerance for generalising statements about Muslims and immigrants, which underpins these actors’ antagonism towards these groups.