Reviewerly/exHarmony huggingface dataset:
Prevalence of Cyberbullying in the New Normal of Learning: Implications to Higher Education Institutions

In the advent of the “new normal” during the pandemic era, strategies to teach and learn switched to online. Students’ behavior and attitude also shifted from face-to-face to online. This study aims to assess the students' profiles and the prevalence of cyberbullying in the higher education institutions in Central Luzon, Philippines. The study used a descriptive-correlational technique with the help of an online survey to gather data. Using a convenience sampling technique, 300 higher education students participated in the online survey during the first semester of 2021–2022. In order to attain the objective of the study, the investigators used a standardized instrument. With the help of SPSS 23, the data analyst analyzed the gathered data using the following statistical tools: frequency, weighted mean, and non-parametrical tests like Kruskal-Wallis, Mann-Whitney U, and Spearman rho. The investigator found that the student-respondents were "never" cyberbullying victims or offenders. Furthermore, statistical inferences showed a variation for cyberbullying offenders as to age and sponsorship/scholarship and a weak indirect relationship between cyberbullying offenders and sponsorship/scholarship characteristics of the students. The investigators recommended pertinent implications for the new normal of learning among students and the institution from the study results.
Read-Write, Peer-To-Peer Algorithms for the Location-Identity Split

Design and the segment table, while hypothetical in principle, have not as of not long ago been viewed as befuddling. Following quite a while of sad research into voice-over-IP, we approve the change of disseminate/assemble I/O, which typifies the instinctive standards of steganography. We affirm not just that robots and SMPs can associate with achieve this goal, yet that the same is valid for repetition
Email Spoofing &amp; Backlashes

The email service is a core platform for Mass communication as a consequence of which, it becomes central Target of all the social engineering and phishing attacks. As a consequence, attackers can try to impersonate or fake a trusted identity to carry out highly sophisticated and deceptive phishing attacks via Email Spoofing. In this work, we analyze: (1) how different Email providers detect and deal with such attacks? (2) Existing protection techniques and what is its scope of effectiveness? (3) Under Which conditions do spoofed emails reach inbox and its potential consequences? (4) Best practices and Adaptability apart from existing methods to remain secure. We address this concern by considering the parameters of top 25 email services (Used by more than billions of users) and also real world experiments. The existing protocols, security layers and the restrictions based on detection methods. The scale of implications by allowing the forged emails to enter the inbox despite getting detected by layers of SPF, DKIM, DMARC and ARC. The extent of problems caused in different paradigms, and the potential of having just SMTP implemented without any additional security layers within the domains. The impact of Misleading UI for allowed spoofed emails by providers is also discussed briefly. We observe the impression of security when users are caught off guard in real world testing on domains (eg. Gmail, Hotmail, Yahoo mail, etc ) by simple platforms to spoof (eg. emkei.cz) apart from discussing the anomalous behavior of gmail as a response. We have conducted experiment to analyze behavior of top email domains against spoofed emails of various types.
Optimizing Python-Based Spectroscopic Data Processing on NERSC Supercomputers

We present a case study of optimizing a Python-based cosmology data processing pipeline designed to run in parallel on thousands of cores using supercomputers at the National Energy Research Scientific Computing Center (NERSC).
Email Spams via Text Mining using Machine Learning Techniques

A lot of data is generated on daily basis which may potentially be useful. This data is generally unstructured and ambiguous to draw a meaning from it. High quality of information can be extracted from this potentially useful data typically through devising of patterns and trends in it. This is done using Text Mining which includes the initial parsing of the unstructured data, processing it and then leading to some meaningful and fascinating information hidden in it. This paper presents the machine learning techniques for text mining that are useful for spam detection in emails.
Prediction of Electromagnetic Measurement Values Using Artificial Neural Networks

Although the development of communication technology increases the quality of life, it causes the increase of electromagnetic pollution. Exposure to electromagnetic pollution above certain limits leads to serious health problems such as cancer and the nervous system. Especially in large settlements such as Maltepe, where the population is dense, studies are carried out to reduce this pollution. In this study, the mapping of electromagnetic field pollution originating from base stations was carried out with 165 measurements taken between 2014-2017. Then, this mapping was trained with artificial neural network and a model was taken for the places where no measurements were taken in Maltepe district and the electric field value was estimated according to the entered values.
A hybrid algorithm for secure cloud computing

Cloud computing is a developing technology that is yet unclear to many security issues. Data in the untrusted clouds can be encrypted using an encryption algorithm. Randomising this data provides more security which can be achieved by padding concept in the cloud. In this paper, the user's data is encrypted using padding scheme, called Optimal Asymmetric Encryption Padding (OAEP) together with Hybrid Encryption algorithm that is based on RSA (i.e., HERSA), to allow multiple parties to compute a function on their inputs while preserving integrity and confidentiality. The homomorphic encryption is performed on the encrypted data without decrypting it in computationally powerful clouds, and the Secure Multi-Party Computation (SMPC) can be used in the cloud to ensure security and privacy of the users. In this paper, we propose a scheme that integrates the Multi-Party Computation (MPC) with homomorphic encryption to allow calculations of encrypted data without decryption. The cryptographic techniques used in our cloud model are described, and the overheads are compared with homomorphic encryption and MPC.
Customer satisfaction-oriented after-sales service node analysis for home appliance enterprise

After-sales service plays an increasingly vital role in home appliance enterprises. Quantitative analysis is supposed to be focused on when researching after-sales service for home appliance rather than qualitative analysis only. In addition, the importance of each node should be distinguished. To solve the two issues, a customer satisfaction-oriented after-sales service index system which can evaluate the quality of after-sales service nodes is presented based on customer satisfaction framework, and the key indexes with crucial influences on customer satisfaction are selected by association rule algorithm. The significance test verifies the effectiveness of the mining results. Besides, the importance of each key node is analysed by random forest. Furthermore, the quantitative analysis of the key nodes is performed by multiple linear regression and the moderating effect of consumption level is verified. Finally, the Kano theory and the prospect theory are employed to explain the results, and optimisation strategies are put forward.
A Cauchy-Gaussian Quantum-Behaved Bat Algorithm Applied to Solve the Economic Load Dispatch Problem

In this paper, a novel Cauchy-Gaussian quantum-behaved bat algorithm (CGQBA) is applied to solve the economic load dispatch (ELD) problem. The bat algorithm (BA) is an acknowledged metaheuristic optimization algorithm owing to its performance. However, the classical BA presents some weaknesses, such as premature convergence. To withstand the drawbacks of the BA, quantum mechanics theories and Gaussian and Cauchy operators are integrated into the standard BA to enhance its effectiveness. Since the economic load dispatch is a nonlinear, complex and constrained optimization problem, its main objective is to reduce the total generation cost while matching the equality and inequality constraints of the system. The validity of the CGQBA is tested on six standard benchmark functions with different characteristics. The numerical results indicate that the CGQBA is effective and superior to many other algorithms. Moreover, the CGQBA is applied to solve the ELD problems on various test systems including 3,6,20, 40,110 and 160 implemented generating units. The simulation results illustrate the strength of the CGQBA compared with other algorithms recently reported in the literature.
Empirical investigation of dimension hierarchy sharing-based metrics for multidimensional schema understandability

Over the last years, quality has gained lot of importance in the development of data warehouse systems. Predicting understandability of multidimensional schemas could play a key role in controlling data warehouse quality at early stages of development. In this area, some effort has been spent to define structural metrics and identity models for assessing quality of these systems. Of the structural properties used to define metrics, aspects of dimension hierarchies and its sharing plays primary role to enhance analytical capabilities of multidimensional schemas, thereby affecting their quality. The authors have previously proposed structural metrics based on aforementioned aspects. The objective of this study is to apply principal component analysis (PCA) to find whether our metrics are improvements over the other existing metrics; and to apply logistic regression to study whether the metrics (selected as relevant in the extracted principal components) combined together are indicators of multidimensional schema understandability. The results of PCA confirm that our structural metrics based on the concept of sharing are different from other such metrics existing in the literature. Further, the metrics selected as principal components can be used in combination to predict understandability of data warehouse multidimensional schemas.
Computers and effective security management

The computer has become an indispensable security management tool, changing information processing and affecting the way security managers work. Chapter 24 talks about the computer most security managers interface with, the PC. Specifically, the topics of hardware, including cloud storage, peripherals, and networks; software, including database management, spreadsheets, desktop publishing, encryption software, graphics, computer-aided design, and geographic information systems (GIS) and global positioning systems (GPS); telecommunications, including electronic mail and messaging, and teleconferencing; the Internet, including online services and social networking; intranets, including specialized programs, integration, and monitoring; artificial intelligence; and multimedia are described. Finally, the risk of using PCs is addressed.
Multi-level SLA violation-based resource allocation in cloud using kernel fuzzy C means with aid of EABC

In our proposed work we are considering four types of agent such as monitoring agent, coordinator agent, user agent and violation agent. Initially, user agent wants to generate the resource in cloud based on the SLA properties and coordinator agent is verifying the request type and SLA properties. The violation of the SLA is a major aspect of cloud computing and the violation leads to the reduction of customer satisfaction level and further affects the cloud provider leading to penalty. Initially the available resources are grouped by kernel fuzzy C means clustering algorithm (KFCM). Then monitoring agent report the resource list to the coordinator agent. Based on the resource list, the coordinator agent allocates the optimal resource with the help of enhanced artificial bee colony algorithm. The performance of the proposed work is evaluated by profit, penalty, cost, execution time and successful execution rate.
DCNet: diffusion convolutional networks for semantic image segmentation

Semantic image segmentation makes a pixel-level classification play an essential role in scene understanding.Recently, most approaches exploit deep learning neural networks, especially convolutional neural networks (CNNs), to tackle the image segmentation challenge.Common issues of these CNN-based methods are the loss of spatial features during learning representations and the limited capacity for capturing contextual information in a large receptive field.This paper proposes a diffusion convolutional network (DCNet) to combine the CNN and graph convolutional neural network (GCNN) for semantic image segmentation.In the proposed model, diffusion convolution is formulated as a graph convolutional layer to aggregate structural and contextual information without losing spatial features.The final segmentation results on the PASCAL VOC 2012 and Cityscapes datasets show better performance than baseline approaches and can be competitive with state-of-the-art methods.
Network and Hypervisor-Based Attacks in Cloud Computing Environments

Cloud computing (CC) has become one of the most transformative computing technologies and a key business avenue, following in the footsteps of main-frames, minicomputers, personal computers, the World Wide Web and smartphones. Its vital features have considerably reduced IT costs, contributing to its rapid adoption by businesses and governments worldwide. Despite the many technological and economic benefits that CC offers, at the same time, it poses complex security threats resulting from the use of virtualisation technology. Compromising the security of any component in the cloud virtual infrastructure will negatively affect the security of other elements and so impact the overall system security. Therefore, to create a practical understanding of such threats, this paper provides an analysis of common and underexplored network- and hypervisor-based attacks against CC systems from a technical viewpoint.
Efficient Immersive Video Compression using Screen Content Coding

The paper deals with efficient compression of immersive video representations for the synthesis of video related to virtual viewports, i.e., to selected virtual viewer positions and selected virtual directions of watching.The goal is to obtain possibly high quality of virtual video obtained from compressed representations of immersive video acquired from multiple omnidirectional and planar (perspective) cameras, or from computer animation.In the paper, we describe a solution based on HEVC (High Efficiency Video Coding) compression and the recently proposed MPEG Test Model for Immersive Video.The idea is to use standard-compliant Screen Content Coding tools that were proposed for other applications and have never been used for immersive video compression.The experimental results with standard test video sequences are reported for the normalized experimental conditions defined by MPEG.In the paper, it is demonstrated that the proposed solution yields up to 20% of bitrate reduction for the constant quality of virtual video.
Students’ self-development through the prism of electronic portfolio

The following paper discusses one of the urgent tasks of higher education at the present stage the development of the personality of a specialist capable of self-development in the conditions of education digitalization in the Russian Federation. The creation of the system of continuous education in Russia is primarily the result of this process, as specialists self-education is a necessary condition for successful employment, career growth, social and professional mobility at the present time. At the same time, the needs and opportunities of various forms of continuing education are unevenly distributed in various industries. So the IT industry requires constant self-education of employees. Practice shows that secondary general education, as a rule, does not develop the necessary skills of graduates self-education. Therefore, the issues of management of bachelors self-development as well as the formation of readiness for continuous education are very relevant for higher education. The paper describes conditions for the development of bachelors readiness to continue education in its various forms (formal, informal) using the electronic portfolio system, which is an integral part of the electronic information and educational environment (EIEE) of the University. The obtained materials can serve as a basis for the development of a single electronic educational space of Russian universities.
Digital Module 12: Think‐aloud Interviews and Cognitive Labs https://ncme.elevate.commpartners.com

Abstract In this digital ITEMS module, Dr. Jacqueline Leighton and Dr. Blair Lehman review differences between think‐aloud interviews to measure problem‐solving processes and cognitive labs to measure comprehension processes. Learners are introduced to historical, theoretical, and procedural differences between these methods and how to use and analyze distinct types of verbal reports in the collection of evidence of test‐taker response processes. The module includes details on (a) the different types of cognition that are tapped by different interviewer probes, (b) traditional interviewing methods and new automated tools for collecting verbal reports, and (c) options for analyses of verbal reports. This includes a discussion of reliability and validity issues such as potential bias in the collection of verbal reports, ways to mitigate bias, and inter‐rater agreement to enhance credibility of analysis. A novel digital tool for data collection called the ABC tool is presented via illustrative videos. As always, the module contains audio‐narrated slides, quiz questions with feedback, a glossary, and curated resources.
Jordanian beauty influencers and their effect on consumers

This paper will investigate the beauty influencers on social media platforms, their behaviours and attributes, and most importantly their effect on consumers. Our hypothesis is that the future of advertisement is social media, the traditional ways are fading, and this shift is facing resistance from the older generations. And with the appearance of advertisement through social media, influential users arose, creating this new phenomenon called 'influencers'. This research aims to investigate the dynamic relationship between the consumers and influencers, and how to control and maintain an ethical relation between any of the two parties most importantly, the influencer's effect on consumers and their behaviour. This study will be carried out through the use of primary research, where a questionnaires been distributed to as many Jordanian beauty consumers to see which influencers attract them the most, the level on influence they are under, and their behaviour in response to this influence.
Scenario approach to the perspective planning of the content of studies with graduate students in the process of mastering the academic disciplines

The article presents an analysis of studies demonstrating the advisability of a scenario approach to the prospective content planning of the training sessions with graduate students mastering the «Methods and methodology of scientific research» course within the framework of the pedagogical phenomenon - improving the training quality of scientific and pedagogical personnel. The aim of the study is to substantiate the use of the scenario approach in the process of preparing graduate students for research and teaching activities along with the approaches used and to prove the advantages of the scenario approach in comparison with traditional approaches in the higher education system. The methodology of the research includes theoretical analysis of foreign and domestic scientific literature on the indicated problems, analysis of various approaches to the prospective planning of the training sessions' content with postgraduate students, questionnaires, generalization and interpretation of empirical data. The article generalizes main ideas of the scenario approach methodology and outlines the opportunities for using the scenario approach for developing the content of studies with graduate students in the process of mastering the academic disciplines.The analysis made it possible to conclude that within the range of the currently used approaches the scenario approach might become one of the most significant pedagogical approaches to the perspective content planning of studies with graduate students. The ideas of the scenario approach implementation and the extrapolation of its methodology to pedagogical problems are grounded.
Improving collaborative filtering's rating prediction accuracy by considering users' dynamic rating variability

Users that populate ratings databases, follow different marking practices, in the sense that some are stricter, while others are more lenient. Similarly, users' rating practices may also differ in rating variability, in the sense that some users may be entering ratings close to their mean, while other users may be entering more extreme ratings, close to the limits of the rating scale. While this aspect has been recently addressed through the computation and exploitation of an overall rating variability measure per user, the fact that user rating practices may vary along the user's rating history time axis may render the use of the overall rating variability measure inappropriate for performing the rating prediction adjustment. In this work, we: 1) propose an algorithm that considers two variability metrics per user, the global (overall) and the local one, with the latter representing the user's variability at prediction time; 2) present alternative methods for computing a user's local variability; 3) evaluate the performance of the proposed algorithm in terms of rating prediction quality and compare it against the state-of-the-art algorithm that employs a single variability metric in the rating prediction computation process.
The Recent Open Problem, Benefit and Challenge of Blockchain Technology in Education

The development of the education sector, especially the University at this time in Indonesia became the main focus of the government, the University was driven to become a leading university not only in the national scale but globally. The graduates of the university became an interesting topic to study because there is a gap between the Education and industry as a graduate user. Blockchain technology now is not only for cryptocurrency, but many researches nowadays believe that Blockchain technology can provide more benefits for universities Several studies mention Blockchain Technology can help in preventing fake diplomas certificate, libraries for borrowing books, and student loans. Those researches are still separated into several resources, so it is difficult to consolidate the benefits and challenges of using the Technology blockchain for universities. Therefore, this exploratory research tries to look for open problems, recent trends of the benefits and challenges of blockchain technology research in education sector as well as looking blockchain technology influence on the important elements in universities, such as people, process, and technology. This research will be conducted using a systematic method by reviewing literature from 9 well-known publishers such as (ACM, SAGE, IEEExplore, Taylor and Francis, Science Direct, Wiley, MDPI, AISEL, and Scopus index) to get a complete picture of blockchain technology in university. The study found the benefits and challenges of using Blockchain Technology and 70% of the important factors is on technology, 15% on people and 50% on processes.
Hackers breach 150,000 Verkada surveillance cameras

Hackers have attacked the live feeds of 150,000 surveillance cameras run by Silicon Valley-based Verkada, and stolen a huge trove of video and image data.