Reviewerly/exHarmony huggingface dataset:
Application of Mamdani’s Fuzzy Inference System in the Diagnosis of Pre-eclampsia

Pre-eclampsia is the second of the top three causes of death in pregnant women after bleeding and followed by infection. By knowing the risk factors, early detection of pre-eclampsia in pregnant womenneeds to be done so that later it can be treated more quickly to prevent further complications. This studyaims to design a practical application of a decision-making system for the diagnosis of pre-eclampsia inpregnant women using the Fuzzy Inference System (FIS) method so it can be used efficiently and effectively for the early diagnosis of pre-eclampsia. The method used in data analysis is the FIS Mamdanimethod with defuzzification using the centroid method. The designed system considers blood pressureand proteinuria as input variables and pre-eclampsia status as output variables. The research resultsshow that the system has 7.27% of Mean Absolute Percentage Error (MAPE) value and when comparing the final diagnosis of the system and expert diagnoses (doctors) from 20 patients at two hospitals, itwas found that the system diagnosis was 100% in accordance with the expert diagnoses.
The place of components in a conditional relationship in the system of causal relations

In linguistics, the linguistic nature of the category of general compound sentences still requires deep recognition. Although it has been somewhat studied from the point of view of structural linguistics, there are still unresolved problems in determining the syntactic nature of complex structures in the relation of a condition. Based on the latest achievements in the syntax of a compound sentence, there is a clear need for a deeper functional and semantic consideration of this language category. This direction, of course, is distinguished by its novelty, scientific depth. In studies, characterized by the manifestation of an active subject – a person who knows the world, reflecting it through language, as a central figure, the linguistic phenomenon is studied from the point of view of the basic principles of its origin, or from the point of view of the principles of cognition of the world through thinking and the representation of these cognitive concepts through language. Of course, in modern science, all this is interpreted as such. In the in-depth knowledge of structural and semantic units in the conditioning relation, the most important thing is recognized as how these conditioning relations manifest themselves in nature and being-being and mental being inherent in humanity, and how they are expressed in language. This article provides for the consideration of conditional components as structures in a causal relationship. In the deepening recognition of structural and semantic units in the conditional relationship, it is recognized as the most important how this conditional relationship manifests itself in nature and creation-being and the mental being inherent in humanity, and how they are reflected in the language.
Monthly and Seasonal Variation of Cloud Cover, Humidity and Rainfall in Lagos, Nigeria

The study of atmospheric variables such as cloud cover, humidity, and rainfall is needed to forecast/predict the weather to enhance policies implemented by the government concerning agriculture, water resources, and other relevant industries in Lagos State (6.45°N, 3.39°E), Southwest Nigeria. There is a need to ascertain the variability in cloud cover with other meteorological parameters in Lagos State which is fastgrowing with a total land mass of 1,171.28 square kilometers. Eleven years (2011-2021) ground data obtained from Visual Crossing a leading provider of weather data were analyzed on a monthly and seasonal basis using statistical tools. The results show that there is a significant rise in the extent of cloud cover in Lagos during July to September, with September being the peak month due to about sixty-three percent (63%) of the sky being cloudy in September. However, there is the minimum amount of cloud cover observed between December and February, with January being the least month about forty-one percent (41%) of the sky cloud-covered in the average year. Lagos experiences a yearly average humidity of 83.5% from June to October, peaks in September (87.88%), while the lowest value (77.26%) occurs in January. The annual average rainfall accumulation for the eleven (11) years is recorded to be 1611.30 mm. In June, September, and October, the rainfall rate is recorded to be very high with values that range between 242.53 mm, 227.25 mm, and 233.86 mm respectively, while December and January is observed to record the lowest accumulation of rainfall with values that ranges between 27.26 mm &amp; 27.97 mm respectively. Finally, the comparison of the linear regression trend and the estimated Pearson correlation coefficient reveals a substantial, positive relationship exists between cloud cover and humidity, although cloud cover has a minor influence on rainfall. According to the study’s findings, it is advised that rainfall awareness programs be expanded and that government policies relating to agriculture, water resources, and other relevant sectors take into account the rising nature of rainfall in recent years.
Ensemble Feature Selection for Age Estimation from Speech

The voice signal carries a wide range of data about the speaker, including their physical characteristics, feelings, and level of health. There are several uses for the estimate of these physical characteristics from the speech in forensics, security, surveillance, marketing, and customer service. The primary goal of this research is to identify the auditory characteristics that aid in estimating a speaker’s age. To this end, an ensemble feature selection model is proposed that selects the best features from a baseline acoustic feature vector for age estimation from speech. Using a feature vector that covers various spectral, temporal, and prosodic aspects of speech, an ensemble-based automatic feature selection is performed by, first calculating the feature importance or ranks based on individual feature selection methods, then voting is applied to the resulting feature ranks to attain the top-ranked subset by all feature selection methods. The proposed method is evaluated on the TIMIT dataset and achieved a mean absolute error (MAE) of 5.58 years and 5.12 years for male and female age estimation. Index Terms— Age Estimation, Feature Selection, Ensemble Selection, TIMIT dataset.
Improve Multi-Object Detection and Tracking for an Automated Traffic Surveillance System

Multi-Object Detection and Tracking (MODT) are essential in many application fields. Still, many enhancements in the speed of detection and tracking were required to overcome the challenges during implementation. This paper presents a new algorithm system for (MODT) to improve the execution time to be robust in real-time applications. A background subtraction detection algorithm with a Kalman filter was used to track and predict the object position and speed parameters. To improve the processing time, its needs to reduce some frames in a way that does not affect the detection accuracy too much and instead use the prediction and the estimated value obtained based on the Kalman filter for the tracked object. This work uses a single video camera to show how effectively to compute and detect multiple objects concurrently; it is applied for daytime preprocessing in an automated traffic surveillance system. Preliminary testing findings show that the suggested algorithm for this vehicle monitoring system is feasible and effective. It illustrates that using the suggested algorithm with a single video camera can simultaneously watch, detect, and track several vehicles and improve execution time. Simulation results on the built system demonstrate that the proposed system reduced the execution time to approximately 41.5% compared to the standard background subtraction algorithm. Results indicate the proposed algorithm has an approximate error for the position and speed of detected and tracked objects compared with the standard background subtraction algorithm. Index Terms— multi-object detection, tracking, background subtraction, Kalman filter, morphological.
Drift Detection and Model Update using Unsupervised AutoML in IoT

This paper addresses the challenges of concept drift on the Internet of Things (IoT) environments and evaluates a machine-learning model's performance under varying data drift conditions using unsupervised Automatic Machine Learning (AutoML) anomaly detection techniques. By implementing a dynamic learning framework and employing advanced analytics, the study showcases the resilience of the proposed methodology against evolving data patterns. The results demonstrate the model's robust predictive capabilities, even in high drift scenarios, underscoring the importance of adaptive models in maintaining effective IoT security measures. The achieved improvement percentages can reach 46% for the F1 score.
Enhancement of Cloud Computing Environment Using Machine Learning Algorithms MLCE

Cloud computing is an evolving and high-demand research field at the forefront of technological advancements. It aims to provide software resources and operates based on service-oriented delivery. Within the infrastructure as a service (IaaS) framework, the cloud offers end customers access to crucial infrastructure resources, including CPU, bandwidth, and memory. When a cloud system fails to deliver as expected, it is referred to as an event, signifying a deviation from the anticipated service. To meet their service-level agreement (SLA) obligations, cloud service providers (CSPs) must ensure continuous access to fault-tolerant, on-demand resources for their clients, particularly during outages. Consequently, finding the most efficient ways to accomplish tasks while considering the rapid depletion of resources has become an urgent concern. Researchers are actively working to develop optimal strategies tailored to the cloud environment. Machine learning plays a critical role in these endeavors, serving as a key component in various cloud computing platforms. This study presents a comprehensive literature review of current research papers that employ machine learning algorithms to propose strategies for optimizing cloud computing environments. Additionally, the survey provides authors with invaluable resources by extensively exploring a diverse range of machine learning techniques and their applications in the field of cloud computing. By examining these areas, researchers aim to enhance their understanding of efficient resource allocation and scheduling, addressing the challenges posed by resource scarcity while meeting SLA obligations. Index Terms— Cloud computing, Resource allocation Prediction, Energy-efficient resource management, Task Scheduling, Machine Learning.
Analysis of Turkey's Cybersecurity Strategies: Historical Developments, Scope, Content and Objectives

Cybersecurity is regarded as a crucial part of overall national security. A national cybersecurity strategy (NCSS) offers direction and a framework for national cybersecurity-related concerns and initiatives. Many nations have devised their own plans to defend against attacks, reduce risks, discourage attackers, and provide a secure and accessible cyberspace to promote innovation, the economy, commerce, and wealth. In order to assist the countries in developing their plans, organizations like the ITU, ENISA, OECD, NATO, e-Governance Academy and Oxford University issued research and advice publications based on the best practices from various countries. The assessment of the scope, objective, and content of Turkish national cybersecurity strategies is limited in academic literature. The studies in the literature that gathered multinational NCSS attributes from various countries and served as a guide for the development of national cybersecurity strategies were examined through a comprehensive literature review in order to assess Turkey's strategies in terms of scope, content, and objectives. Following the development of assessment criteria based on these studies and research, a qualitative study was carried out to evaluate and ascertain the degree of conformity of Turkish cybersecurity strategies with the criteria. The historical context of Turkey's cybersecurity strategies was explored in this article, along with strategy revisions, evaluations of the strategies' scope, content, and objectives, and gaps that should be filled to make the strategies more effective.
DE-JSMA: a sparse adversarial attack algorithm for SAR-ATR models

The vulnerability of DNN makes the SAR-ATR system that uses an intelligent algorithm for recognition also somewhat vulnerable. In order to verify the vulnerability, this paper proposes DE-JSMA, a novel sparse adversarial attack algorithm based on a salient map's adversarial attack algorithm and differential evolution algorithm, with the synthetic aperture radar (SAR) image feature sparsity considered. After accurately screening out the salient features that have a great impact on the model inference results, the DE-JSMA algorithm optimizes the appropriate feature values for the salient features. In order to verify its effectiveness more comprehensively, a new metric that combines the attack success rate with the average confidence interval of adversarial examples is proposed. The experimental results show that DE-JSMA extends JSMA, which can be used only for targeted attack scenario, to untargeted attack scenario without increasing too much time consumption but ensuring a high attack success rate, thus achieving sparse adversarial attack with higher reliability and better sparsity in both attack scenarios. The pixel perturbations of only 0.31% and 0.85% can achieve the untargeted and targeted attack success rates up to 100% and 78.79% respectively.
Trailblazing Scholar, Brilliant Critic

This essay is an assessment of and a tribute to Yingjin Zhang’s manifold academic achievements. He has made tremendous contributions to multiple fields: film studies, modern Chinese literature, comparative literature, urban studies, media studies, Republican China studies, as well as other related areas of inquiry. His scholarly impact has been enormous. The essay focuses on two aspects of his scholarship: First, his leading position in the establishment, institutionalization, and internationalization of academic fields like Chinese cinema studies, modern Chinese literary studies, and Chinese comparative literature. Second, his vital role as an influential, astute theorist in rethinking the theoretical parameters and critical tools of the aforementioned disciplines. While he was shaping and reshaping academic fields through producing monumental books, he was keenly aware of the permeable disciplinary boundaries of ever-changing critical terrains. Just as he was establishing Chinese literature and cinema, transnational studies, comparative literature, and world literature, he was at the same time re-examining and reflecting on the feasibility of these terms in a new critical context. A brave, open-minded, self-reflexive spirit is characteristic of his scholarship as a whole. He did not take an entity for granted, but cross-examined and redefined it according to changing historical and social circumstances.
University Pedagogy Evaluated by Massification and the Metaverse

Learner's access to an immersive, platform-like digital universe would be seen as an invitation to exist in a different way through a process of routine communication with a mentor (tutor) who enables him or her to break out of anonymity and express himself or herself using tools built into the hosting platform. The characteristics and purposes for using a MOODLE-like platform are obviously different from those of 3D virtual worlds. By fostering relational dynamics that encourage students to revalue their personal and academic identities by reviving their digital identities, the authors show how far the hybrid course can counteract the negative effects of massification. Defining the conceptual and theoretical frameworks, as well as a postulate and a problematic, will be done first in order to provide an account of this recent experience. Before presenting and discussing the findings, they first go over the methodology used to gather and analyze the information from students, both in-person (via the placement test) and virtually (via activity reports and satisfaction surveys).
Rapid Learning in the Teaching of Physical Education and Sport

Integration of information and communication technologies in education has become a significant phenomenon that provides educators with the resources they need to improve teaching and learning processes. When used on a regular basis, ICTE promotes program objectives and increases student engagement in learning. This can lead to learning experiences that are rapid, interactive, and impactful. Furthermore, the usage of ICTE increases student motivation and simplifies the teaching process. In recent years, there has been considerable growth in the use of tools designed to promote quick learning in many different kinds of countries. These innovative methods aim to simplify the teaching and learning process by speeding up acquisition and decreasing time spent on traditional explanations. Teachers are invited to use these tools to improve the effectiveness of their instruction.
Teachers and Schools Navigating Virtual Worlds and the Metaverse for Enhanced E-Learning

The purpose of the study reported on in this chapter is evaluating teachers' and schools' capacities to integrate technologies into a new educational curriculum using an e-capacity model approach. Against the background of navigating virtual worlds and the metaverse for enhanced e-learning, the chapter will discuss issues with regard to effective strategies and policies for digital integration in education and training, societal issues (social inequality and the digital divide, etc.), legal and ethical issues (intellectual property legislation, etc.), the hybridization of training (hybrid, Hy Flex, co-modal, etc.), simulations, artificial intelligence (AI) for training, the educational metaverse, and information and communication technologies (ICTs).
Getting Started With Augmented Reality (AR) in Inclusive Online Teaching and Learning in Higher Education

With the mass-scale adoption of augmented reality (AR) in the commercial space, and student interest in 3d- and 4d experiential learning, higher education is starting to look towards digital augmentations of real spaces for teaching and learning. University creative shops are exploring whether they can get into the game of producing AR-enhanced experiences: campus tours, interactive gaming, virtual laboratories, exploratory art spaces, simulations, design labs, online / offline / blended teaching and learning modules, and other AR applications. This work offers a basic environmental scan of the AR space for inclusive online teaching and learning, and it includes pedagogical design leads from the current research, technological knowhow, hands-on design / development / deployment of learning objects, and online teaching and learning methods. This work does not take a pedagogical theory approach although several theories are mentioned. Rather, the focus is on applied AR in teaching and learning.
The Power of AI for the Sake of the Metaverse in Education

The potential for immersive, individualized, and interactive learning experiences for students has made the use of metaverse technology in education an extremely popular subject. This virtual reality environment, “the metaverse,” allows users to communicate with other users in a virtual environment by utilizing tools like augmented reality, artificial intelligence, blockchain, and machine learning. Metaverse technology offers higher levels of creativity and customization than conventional e-learning techniques. It enables improved user-environment interaction and can replicate emotional and cognitive processes that are more similar to those encountered in face-to-face learning environments. By providing opportunities that would ordinarily not be available, this innovation creates new chances for enhancing established learning activities. The use of metaverse technology in education has the potential to completely transform the educational process by providing students with an immersive, personalized, and interactive environment.
Hybridization of Approaches and Gradual Investment in ICTs Toward Innovative University Pedagogy

This research aims to reflect on the integration of digital technologies in teaching French within non-linguistic disciplines. The goal is to think about the academic integration of French in light of various contexts and practices, such as hybrid teaching (both in-person and online via learning platforms dedicated to Edmodo, Moodle, or even MOOCs), combining theory and practice while considering constraints like motivation, organization, and materials. The authors start from the idea that students' poor performance at the university is due to the extensive use of the French language in courses spread over three months, with three hours of class per week. Therefore, various approaches were tested, with a gradual implementation of a hybrid approach known as light in-person classes. Since the fall of 2014, the team of professors has tested three approaches, ranging from enriched in-person classes to 100% online MOOCs. The latter was conducted in the spring of 2016 through a partnership between the Faculty of Sciences at the University Mohammed V in Rabat and the French Institute of Rabat.
Exploring the Emerging Potential of the Metaverse in Education

This chapter explores the transformative integration of virtual worlds, particularly the Metaverse, into the field of education. It delves into the evolving pedagogical strategies that capitalize on immersive, interactive learning experiences, catering to the preferences of today's digitally native students. These virtual environments enable educators to craft dynamic, personalized curricula and empower students to become active participants in their educational journey. The chapter also highlights the importance of digital proficiency for both educators and students in navigating these virtual realms. While the benefits of the Metaverse in education are evident, challenges related to infrastructure, privacy, security, and ethics are addressed. In general, this chapter compressively provides an analysis of the evolving landscape of education within the Metaverse.
Review: Dual Method Cryptography and Steganography in Video Frames in IoT

The Internet of things (IoT) is one of those emerging technologies, which are going to rule the world in the next few decades. The IoT environment not only enables Human to Machine interaction but also fosters Machine to Machine connectivity. Numerous IoT devices have poor security and insufficient computing power, making them prime targets for hackers. The IoT environment uses lightweight cryptographic techniques to address security requirements. Another security method for IoT devices is steganography. In the contemporary Internet era, the ability to secure private information is crucial, and steganography offers this capability. Due to its great ability to conceal sensitive data, video has drawn the attention of numerous academics among all forms of digital media. The main goal of this work is to examine several methods for fusing video steganography and cryptography techniques. Additionally, a thorough investigation and evaluation of a variety of video steganography methods in both compressed and raw domains are also emphasized. The comprehensive analysis of prior material makes it easier to have in-depth knowledge while creating approaches that combine cryptography with steganography. Index Terms — IoT, Lightweight, Video, Cryptography, Stenography