Reviewerly/exHarmony huggingface dataset:
Emerging Requirement Engineering Models: Identifying Challenges is Important and Providing Solutions is Even Better

Requirement Engineering is one of the most crucial tasks because it serves as the foundation for any software.Four pillars of requirement engineering procedures underpin the entire software.The bricks that make up the software edifice are functional and non-functional needs.Finally, design, implementation, and testing add stories to the foundation, allowing a full software tower to be built on top of it.As a result, the foundation must be strong enough to support the remainder of the software tower.Requirement engineers have various hurdles to design successful software for this purpose.Requirement Engineering (RE) is emerging as an increasingly important discipline to promote the development of web applications, as these are designed to meet various stakeholder requirements, additional functional, information, multimedia, and usability requirements compared to traditional software applications.The requirements of software systems are a very important area in software engineering.The success of software systems depends on how it effectively meets the requirements of users.In this paper, the review of current state of requirements engineering in which requirements from users are checked analyzed with their consistency and correctness is presented, and then identifies the emerging models of requirement engineering.Firstly, the paper highlight the current activities that enable the understanding of goals and objectives for developing proposed software systems, then the focus is on the techniques for improving the precision, accuracy, and variety of requirements.Next, identification of the challenges of emerging requirement engineering models is explained.The challenges like security and global trend that posed by emerging models of the future.Finally, we are trying to suggest some solutions for the mentioned challenges.
Unified modeling of the spread of the virus in urban areas

This work presents a developed and implemented software package that simulates the spread of a pandemic in urban agglomerations, using the example of coronavirus infection. This program allows you to predict the spread of the virus in order to regulate and manage the process of the epidemiological situation, and to carry out further measures to combat it.
Optimizing Task Processing in Big Data with Federated Learning

With the rapid development of cloud computing and big data technology, task processing based on edge computing and fog computing in big data has become a research hotspot nowadays. Most related research work is based on modularized program and existing intelligent algorithms. However, there are two bottleneck problems: firstly, many existing task processing frameworks are always based on optimization algorithm, which requires multiple iterations to achieve the optimum target searching, which is often a time-consuming process; secondly, there always be data island problems among task processing clusters, which becomes the bottleneck of data sharing during big data analysis. To solve this problem, based on the idea of fast task migration, this work proposes a task processing approach based on federated learning and deep feature learning in big data to achieve accurate task classification and rapid task allocation named FLOM (Federated Learning based Optimization Methodology for large-scale tasks processing). We employ federated learning framework to realize data aggregation and establish personalized models. Deep feature learning is exploited to train task classification models based on deep neural network. Experiments based on Spark show that FLOM can effectively achieve accurate task classification and improve the performance in terms of overall load balancing effect.
SELECTION OF KEY CONTROL ZONES IN THE GEOTECHNICAL MONITORING SYSTEM

In this paper, we propose an algorithm for identifying key zones of geotechnical monitoring based on the data of an information and analytical system. The proposed algorithm is based on the analysis of the stability of individual sections of the geotechnical system, group stability using a neural network approach and a basic model. The training sample is formed on the basis of the geotechnical system model and a priori information. To isolate homogeneous reactions of a geotechnical system at different values of its parameters, the data clustering procedure is used. The correction of the permissible limits of the output of the controlled parameters is proposed to be carried out taking into account the results of the bifurcation analysis. The practical verification of the proposed algorithm was carried out on the monitoring data of a three–story building. The results obtained during the processing of the data of the information and analytical system of geotechnical monitoring by the proposed algorithm are consistent with the measurement and modeling data.
Retraction: CSTM: Cluster-based Security Trust Mechanism for VANET (IOP Conf. Ser.: Mater. Sci. Eng 688 044051)

This article has been retracted by IOP Publishing following an allegation that this article heavily plagiarises another article by a different author group [1]. IOP Publishing has investigated in line with the COPE guidelines, and agree the article constitutes serious plagiarism. IOP Publishing wishes to credit the anonymous whistleblowers for bringing the issue to our attention. To date the authors of the article have not responded to our requests for an explanation, if the authors wish to contest this retraction they are welcome to contact researchintegrity@ioppublishing.org . 1. A. Kchaou, R. Abassi and S. G. El Fatmi, "Towards a Secured Clustering Mechanism for Messages Exchange in VANET," 2018 32nd International Conference on Advanced Information Networking and Applications Workshops (WAINA), 2018, pp. 88-93, doi: 10.1109/WAINA.2018.00068 Retraction published: 30 November 2021
Strategies for facilitating creative music collaboration online

Online collaboration between musicians in 2020 is a rapidly developing practice due to a range of environmental, epidemiological and creative motivations. The technical facility to collaborate in a variety of different formats exists via file-sharing services, video conferencing suites and specialist music services such as Splice and Auddly. Yet, given this proliferation of technologies, little attention has been paid into how creative musicians can most meaningfully utilize these new collaborative opportunities within their working practice. In this article, we wish to share some reflections from a case study of online music collaboration gained through our experience of facilitating three online songwriting camps with students from Leeds Conservatoire in the United Kingdom and Rhythmic Music Conservatory in Denmark. This article will particularly focus on the importance of managing roles, the impact of communication tools and the requirement for time management when collaborating online before proposing a set of guidelines derived from this study to help enable productive online creative collaboration.
Research on Insulator Detection Method Based on Scene Recognition

Target recognition of insulators is the prerequisite for the condition assessment of insulator equipment. Accurate identification of insulators is of great significance to insulator maintenance. This paper combines infrared images and machine learning to propose an infrared image insulator detection method for scene recognition including image preprocessing, model prediction and image fusion; construct a target detection model through the structure of encoding and decoding in semantic segmentation, which can accurately identify insulators. The accuracy of the insulator detection method based on scene recognition proposed in this paper has reached 99.6%, while the traditional target recognition method is 44%. It provides solutions for field applications in the field of embedded devices and intelligent robots.
Improved classification and grading of interferents in serum specimens using machine learning

Serum specimens containing interferents affect the accuracy of test results through a variety of mechanisms. Consequently, rigorous quality control of serum samples before biochemical analysis may help prevent incorrect results. Based on the hypothesis that serum color images contain information about the category and concentration of interferents, a machine learning method was proposed to automatically classify and grade color images of serum samples into three categories and five levels of interferent concentration. First, using a color correction method, the color image was preprocessed to eliminate ambient light color cast during the shooting process. Serum regions were then segmented using a convolutional neural network. Subsequently, color moment features were extracted and utilized in the classification of hemolysis, icterus, and lipemia (HIL), the three most common interferents in blood examinations. Finally, feature selection was utilized to select the most suitable features for grading the degree of HIL. This feature subset was used to grade five concentration levels for each category. The Dice coefficient and IoU of the serum region segmentation results were 96.36% and 93.02%, respectively. The accuracy and F1-score for classification were both 1. For the grading task, the accuracies were 0.9829, 0.9876, and 0.9526, and Fl-scores were 0.9828, 0.9876, and 0.9520 for hemolysis, icterus, and lipemia, respectively. The proposed method can successfully identify if a sample contains HIL interference and grade the degree of interferent concentration, providing an efficient and feasible method for serum quality control.
Number Theory

This chapter introduces some of the fundamental concepts in number theory, with an eye towards applications in the cyber domain. Topics covered include divisibility, modular arithmetic, prime numbers, multiplicative functions, properties of polynomials, primitive roots, discrete logarithms, and the problems of primality testing and integer factorization. Along the way, we discuss applications including cyclic redundancy checks, the RSA cryptosystem, and Diffie-Hellman key exchange.
Handwritten Image Segmentation Carakan Madura Based Projection And Connected Component Labeling

At the time the Carakan Madura is currently found on sign of street names in the Madura region. It is also found in the historical sites and museum. However, many people do not understand about Carakan Madura because it is rarely used in daily life. There are many ways to keep Carakan Madura sustainable and not extinct, including preservation and maintenance of historical objects and also from education. The maintenance of these historical objects is usually carried out in museums and places where historical objects, including the Sumenep palace museum and Asta Tinggi. In the Sumenep palace museum, documents with Carakan Madura are still stored. In the Asta tinggi there are also stone carvings with the words Carakan Madura. There are other important things in the preservation of Carakan Madura, namely that Carakan Madura is included in the local content education curriculum for Madura language so that students can learn about Carakan Madura properly and well so students are not only knowing, but also understanding. For this reason, tools are needed so that students can more understand the Carakan Madura. The purpose of this research is to segment of the Carakan Madura handwritten image based on projection and connected component labeling. The results of this research can be concluded that the segmentation process in Carakan Madura handwriting has been successfully carried out in the form of the composing characters of Carakan Madura.
How Do Speakers Pause and Hesitate in English and Japanese? - A Comparison Using Parallel Corpora of English and Japanese Presentation Speeches -

We built an English presentation speech corpus, COPE, to enable contrastive studies of speech disfluencies, particularly fillers, between English and Japanese. Recording settings and linguistic labels are similar to those of informal presentation speeches in CSJ, a huge Japanese spontaneous speech corpus. We introduce COPE in the first section, followed by contrastive studies on silent and filled pauses using COPE and CSJ. The first study revealed larger silent pause duration ratio and higher filler frequency per word in Japanese than in English. The second study indicates that silent pause durations at sentence and clause boundaries are relevant to the immediately following clause initial filler probabilities in both languages. More clauses are preceded by clause-initial fillers after longer silent pauses. The results support the hypothesis that fillers are used to fill uncomfortably long silence.
Collaboration and advance planning across campus create more cybersecure colleges, universities

“Cybersecurity is a threat that changes all the time, so addressing it involves everybody living and breathing security as second nature.” This was a key theme of the recent National Student Clearinghouse webinar titled “CyberSecurED: Building a Cyber‐Ready Campus.” The webinar panelists — experts from EDUCAUSE, the Cyber Readiness Institute, REN‐ISAC: Research and Education Networks Information Sharing and Analysis Center, and NSC — discussed best practices for preventing and responding to cyber‐attacks.
MELTWEB: A Transient Execution Attack to Capture Data in Fill Line Buffer

The out-of-order execution mechanism is widely used in modern processors. However, meltdown attacks exploit this mechanism to allow malicious instructions to capture sensitive data from kernel domains. This work aims to present a new meltdown attack, called Meltweb. It is a new approach in the category of privileged speculative execution attacks to leak arbitrary data into address spaces and privilege limits. Meltweb can be implemented from linear execution without the need for page faults, eliminating the need for an exception suppression mechanism, thus allowing the attack to be executed throughout the system of arbitrary code without privileges as in search engines interpretation of Javascript. To exemplify the performance of the attack, two proof-of-concept variants were developed that demonstrate the ability to perform the attack natively, as well as remotely using Javascript. The attack developed showed satisfactory results in its two variants, with 98% accuracy in capturing confidential data in the native variant. For the remote variant, the attack proved to be capable of capturing navigation data as a URL recovery with an error rate of 2.3%.
Trends in Text to Image Synthesis (T2I) using Generative Adversarial Networks

The two most evident modalities of humans are language and vision.Any system that aids interaction between human beings and Artificial Intelligence (AI) is rooted upon these two.Text-to-Image synthesis (T2I) powered by Natural Language Processing (NLP) and deep Generative Adversarial Networks (GANs) replicates this phenomenon.The logical relationship between semantics and vision guides T2I, that attempts to translate highly detailed natural language textual descriptions to pixel-level details.The human concept of attention is leveraged and conceptualized by deep attentional multi-layered GANs.Mimicking the human thinking processes of visualizing the scenes in mind while speaking and listening can be extensively used in various AI applications that craves brain-like comprehending potency.The advancement of a multitude of GANs that focused on semantic consistency, high-resolution photo-realistic images and diversity in synthesis has been investigated in this article.
Development of S core Management Software for D ance Sport Competition

In recent years, the development of scoring management software for recording competition data has also been put on the agenda and gradually applied to all kinds of competitions, and has also attracted the attention of many professionals. At the same time, because of the rising of sports dance competition, many people associate sports dance with score management software. The connection between them also makes the sports dance competition more and more standardized. This paper will also analyze and study the score management software to explore the significance and characteristics of the score management software, and also put forward the difficulties in the use of score management software in sports dance competitions. Of course, this paper also explores how to better use score management software in sports dance competition, and the application of score management software in sports dance competition also lays a foundation for its development and perfection.
Full Issue

Africa Research Bulletin: Economic, Financial and Technical SeriesVolume 56, Issue 7 p. I-I Full Issue Full Issue First published: 04 September 2019 https://doi.org/10.1111/j.1467-6346.2019.09027.xAboutPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Share a linkShare onFacebookTwitterLinked InRedditWechat Volume56, Issue7September 2019Pages I-I RelatedInformation
Introduction of video resources in interactive learning of students specialty in chemical technologies and engineering during the covid-19 pandemic

The main problems and difficulties that arose in the educational process after the introduction of quarantine regulations during the COVID-19 pandemic are considered. The most common online platforms used by teachers to conduct classes in video conferencing are described. The expediency of improving the methods of distance learning of students is noted. The main attention is focused on the technological advantages of the introduction of video resources in the interactive training of students of chemical and technological specialties, which is due to the specialization of the authors.
Enhancing Interactivity in Propp-Based Narrative Generation

In this paper, we recall the main limitations of interactive storytelling (IS) systems based on Propp’s Morphology of the Folktale, in particular with respect to the degree of interactivity that they offer, and propose an adapted morphology. Namely, we allow the original Propp functions to have a reversed outcome, we study how to combine sub-plots, and we introduce the use of flashbacks. We claim that such modifications enhance the interactivity of Propp-based IS systems.
Rigorous light-scattering simulations of space-weathering effects on reflectance spectra

&amp;lt;p&amp;gt;We present a multi-scale light-scattering model that is capable of simulating the reflectance spectra of a regolith layer. In particular, the model can be applied to a case where the regolith grains have varying amounts of nanophase inclusions due to space weathering of the material. As different simulation tools are employed for different size scales of the target geometry (roughly, nano-, micro-, and millimeter scales), the particle size effects, the surface reflections, and the volume scattering can all be properly accounted for. Our results with olivine grains and nanophase iron inclusions verify the role of the nanoinclusions in the reflectance spectra of space-weathered materials. Together with the simulation results, we give simplified explanations for the space-weathering effects based on light scattering, namely the decrease of albedo, the general increase of the red spectral slope, and the dampening of the spectral bands. We also consider the so-called ultraviolet bluing effect and show how the change in the spectral slope over the ultraviolet-visual wavelengths is due to the decrease of reflectance in the visual wavelengths rather than the increase of reflectance in the ultraviolet part.&amp;lt;/p&amp;gt;
Quantitative Analysis of Facial Symmetry Among Different Expressions

Facial expression recognition technology has gradually become a research hotspot. Facial symmetry is an important facial feature. This paper quantitatively analyzes the differences of various facial symmetry indexes among different facial expressions. Firstly, image preprocessing standardizes the images and finds out the landmarks. Then five asymmetric features of the angle of key landmarks, regional asymmetry, centroid, singular value, and structural dissimilarity are extracted from the facial images. The experimental results show the following conclusions. The asymmetric features of happy are more aggregated than other expressions. The mild asymmetry accounted for the largest proportion of happy. The moderate asymmetry accounted for the largest proportion of most expressions. The severe asymmetry of all expressions accounted for the least.
Communication System For Non-Verbal Paralyzed Patients Using Computer Vision

This paper presents a solution for assisting the paralyzed patients in their day to day lives, by integrating a system that would be controlled by their eyes. The presented system contains a set of different messages in a variety of languages that the user can select from without any speech or physical movement, other than eyes. This system uses computer vision to translate the movement of eye-ball to the cursor on the screen, and eye-blinking to register clicks on the computer to trigger the desired output. The technique used to implement object detection in the following system is called HOG (Histogram of Oriented Gradient). The system is tested primarily on two datasets, one of which is an open-source dataset containing pictures of people, obtained from Kaggle, and the other one is collected locally at DHA Suffa University.
MPSK Signal Modulation Recognition based on Decision Fusion

Many scholars have done a lot of research on the modulation recognition of signals, and have proposed various modulation recognition methods. However, in the commonly used recognition methods, the recognition effect of the multiple phase shift keying signal (Multiple Phase Shift Keying, MPSK) is not ideal. Therefore, a decision fusion method based on a deep learning model is proposed. Firstly, I/Q modulation is used to obtain the MPSK modulated signal, and then through signal preprocessing, the time-frequency diagram and constellation diagram of the modulated signal are received. Next, the feature extraction and recognition are performed through the convolutional neural network, and finally, the results of the two types of feature recognition are combined with DS evidence theory to obtain the final recognition result. It can be seen from the simulation experiment that under different signal-to-noise ratios, the recognition effect of the time-frequency diagram and the constellation diagram has its own advantages, and each advantage can be used to make decision fusion, strengthen the generalization ability of the recognition model, and make the recognition effect of MPSK signal better. And the recognition accuracy of decision fusion is higher than 90%, when at the high signal-to-noise ratio.