Reviewerly/exHarmony huggingface dataset:
Internet of Things and Blockchain for Monitoring of Sustainability of Smart Cities

The use of advanced technologies can help the smart city in the digital age develop into an egalitarian system. By exploiting advancements in developing technology, the smart city in the electronic era may develop into an intelligent community. Especially, a digitally enabled smart city environment has emerged as a result of the increasing use of blockchain-based. Several blockchain applications aim to address issues in a variety of industries, including currency or regulatory compliance with the Internet of Things (IoT) including economic and social services. Additionally, the fusion of blockchain with artificial intelligence (AI) is transforming the network topology of smart cities to generate sustainable environments. To create sustainable smart cities, however, these technological breakthroughs present both possibilities and difficulties. The adoption of blockchain networks in smart cities is characterized by security concerns and other difficulties, which are thoroughly reviewed in this study. To create a sustainable smarter society, several important elements for the integration of blockchain & AI technologies are discussed in detail in this study. The study examines blockchain's enhanced security options by outlining the key concepts that can be used to build various blockchain-based, AI-based smart transport systems. The study also discusses the issues that remain unresolved and the direction of ongoing research, which results in new security standards and recommendations for a long-term smart city environment.
Identification of Sickle Cell Anemia by Employing Hybrid Optimization and Recurrent Neural Network

Red blood cell deficiencies are the root cause for anaemia. In sickle cell anaemia, the red blood cell's (RBCs) shape shifts into a crescent or sickle form. It takes a lot of time and effort to manually examine tiny images. Red blood cells have complicated and varied forms, and their overlapping cells make categorization and counting of them difficult. In this study, sickle cell identification in microscopic images is automated. The RBCs are then classified into three forms: round, elongated (sickle cell), and other shapes using image processing and deep learning approaches. Sickle cell illness fatalities can be decreased and the condition may be managed successfully with early discovery. In order to identify sickle cell disease, hybrid optimization, and recurrent neural networks have been developed. This technique first gathers images of blood cells. The image is enhanced by using the wiener filter during the initial processing step. Images of red blood cells are then segmented using Otsu thresholding. The hybrid whale and particle swarm optimization (WPSO) technique is used for extracting the characteristics from images. In order to categorize the RBC cells, a recurrent neural network (RNN) classifier is utilized. A variety of factors, including precision, accuracy, recall, and f1-score, are used to evaluate the efficiency of the suggested approach. The results of the research have been compared with other methods now in use. In comparison to current techniques, the suggested approach offered more accuracy and precision. Saving people's priceless lives would be made possible by this automated detection method.
An Overview on Breast Cancer Detection &amp; Classification Methods Using Machine Learning Techniques

Breast cancer is a prevalent and deadly disease that primarily affects women. Breast cancer has claimed the lives of many people all over the world. Early breast cancer detection, on the other hand, can significantly improve a woman's chances of survival. There are various approaches and procedures for detecting cancer in breast tissue. Breast cancer may be detected using Image Processing, Machine Learning, and Deep Learning methodologies and techniques. People who use trustworthy techniques and make better selections will be able to discover breast cancer in its early stages and save a woman's life. This research aims to contemplate and make inferences from diverse studies on the subject of classification using Machine Learning approaches. The researchers and physicians who devote their time and expertise to enhancing breast cancer screening methods will definitely profit from this research study.
Face Detection and Adaptive Zooming for Intelligent Lecturer Tracking System

Distant learning has gained much importance during the global pandemic. Due to the restriction on offline classes, universities and schools were compelled to adapt to the online teaching mode in which live classes and recorded sessions played the key role. For this purpose, an intelligent lecturer tracking system is developed for improving the quality of lectures. In this research study, a face detection technique with adaptive zooming for an Intelligent Lecturer Tracking and Capturing system is proposed. Real-time video is captured and an adaptive zooming technique is applied to maintain a stable output of the lecturer.
Finger Print Voting System Using Minutiae Algorithm

Android applications are used in the development of the Smart Voting System. Each voter's facial image, finger print, and voter ID are required by the programme in order to guarantee their individuality in the system. The election committee's manual labour is decreased by this system enables easy voting and authenticates the voter to prevent loitering or voting fraud. It guarantees that the voting process cannot be changed by an unauthorized individual. Using gathered data that is kept in a centralized database data, the voter authentication may be done in real time. The suggested dual authentication method employs facial detection as its second level of verification after verifying the finger print as its first stage. The security of the voter data is another priority for this smart voting system. By requiring all citizens to register to vote even after being mobilized, this helps to increase the voting rate.
Geologs: A Complete end-to-end Classroom Management System

This classroom management system is designed to simplify attendance tracking and data access. It replaces the traditional pen-and-paper method with a location-based tracking and QR code system, providing administrators with full control over student attendance, classroom routines, and location-based statistics. The proposed system is designed to save time for professors by eliminating manual processes and reducing their workload. Students can access their attendance reports through a detailed dashboard that displays visual statistics and notifications. The system utilizes GPS technology to determine the location of students on campus. If the location of a student and the assigned classroom or block are the same, the system confirms attendance. QR codes are used as a secondary measure to confirm attendance, with randomly generated codes requiring location verification when scanned. This ensures the system is not easily susceptible to malpractices. Administrators have access to a dashboard that displays detailed attendance records of all students, enabling them to override attendance records in exceptional cases. By simplifying attendance tracking, reducing manual processes, and providing easy access to attendance data, this classroom management system streamlines administrative tasks and helps professors focus on what's most essential.
Air Quality Hotspot Monitoring with Trajectories of IoT in Smart City Implementation

In the era of 21 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">st</sup> century development in industrialization and timely updating technology increases the people needs. Transportation application especially private transport have also increased. This cause emission of greenhouse gases like CO, CO2, NH3, NO, PM2.5 etc. leads to air pollution. Air pollution is serious environmental challenge in our daily lives. Lowering air quality adversely impacts on public health and environment. Measuring and monitoring air quality with the help of deploying IoT based sensors network at particular regions. In this study, AQMS is implemented for detecting air pollution hotspot and identifying source of trajectories. Real-time air quality monitoring system helps to monitor sudden change in the atmosphere. Machine Learning techniques analyze the collected data which helps us to predict climate change. This model reduces complexity and improves efficiency and feasibility and can provide more reliable and accurate decisions regarding smart city development, industrial and transportation management, public health, the environment.
Cyber Bullying and Toxicity Detection Using Machine Learning

The increased use of online platforms for communication has made cyberbullying and toxicity detection a critical issue in recent times. This paper explores the topic of cyberbullying and toxicity detection and proposes potential solutions for identifying cyber violence and offensive language more effectively. According to the study of the algorithms in examined research papers on cyberbullying and toxicity detection, this research study presents a novel approach that achieved 90% accuracy in identifying bully text in social media comments. This is done by using machine learning algorithms such as SVM, Logistic Regression, Naive Bayes, KNN, and Random Forest, with SVM and Random Forest exhibiting the best performance. Additionally, the system improved the accuracy of identifying bully images in social media posts to 84.5% by using the MobileNetV2 model (DNN), which is superior to other approaches. The system is trained using a large, labeled dataset of text data to identify and classify different types of cyberbullying and toxic content. The findings suggest that the proposed models hold promise in detecting instances of cyberbullying and offensive content effectively. These results have significant implications for the development of cyberbullying and toxicity detection systems. The proposed approach can be integrated into various social media platforms and online communities to identify and mitigate cyberbullying and toxic content more efficiently. The study also highlights the need for continued research and collaboration among stakeholders to address cyberbullying and toxicity effectively.
Data Center Organization and Optimization Strategy as a K-Ary N-Cube Topology

Data center deployments are increasing because of the ongoing expansion of IoT environments, which require data centers of a small to medium size, where forwarding traffic is optimized in order to save energy consumption. In this paper, a data center organization and optimization strategy by using a k-ary n-cube topology has been proposed as such a design provides an easy manner to deal with traffic, which also has the advantage of presenting a collection of redundant paths to undertake alternative ways between any pair of nodes, thus avoiding single points of failure. Furthermore, a formal algebraic model referred to this topology has also been specified and verified.
Convolutional Neural Network and Long Short Term Memory on Inertial Measurement Unit Sensors for Gait Phase Detection

Recent developments in robot exoskeletons have significant attention due to their ability to enhance mobility. Gait phase detection is a critical component of the control system for these devices, as it enables the identification of the current walking stage and facilitates appropriate ankle functionality. Gait phase detection has increasingly favored the use of inertial measurement units, which provide valuable data on angular velocity and acceleration. Traditional approaches, such as Hidden Markov Models, have been employed for gait phase detection. However, incorporating long-term dependencies into it can be challenging. An improved gait phase detection algorithm was presented in our article that exploits long-term dependencies by combining Convolutional Neural Networks with Long Short-Term Memory. The research aims to develop and evaluate models that can effectively incorporate previous values, enabling a better understanding of the temporal dynamics of gait. First of all, data from seven participants were collected using a gadget with an Arduino Nano 33 IoT attached to their feet. Performance evaluation was conducted using metrics, such as accuracy, precision, recall, and F1 score. The model successfully identified gait phases with an F1 score of 93.33%. The algorithm classifier’s architecture enables fast adjustments to various gait kinematics using only one sensor attached to the foot. The findings of this study contribute to advancements in gait analysis, leading to improved control and adaptability of assistive devices, and enhancing rehabilitation therapies and prosthetic device development.
Navigating Ambiguity: Imprecise Probabilities and the Updating of Disease Risk Beliefs

Probabilistic risk beliefs are key drivers of economic and health decisions, but people are not always certain about their beliefs. We study these "imprecise probabilities", also known as ambiguous beliefs. We show that imprecision is measurable separately from the levels of risk beliefs. People with higher levels of imprecision update their beliefs more in response to a randomized information treatment, and new information also causes changes in imprecision levels. We show that we can map our data onto both a standard Bayesian model and a version that is designed to handle imprecise probabilities; these models can match some features of our data but not all of them. Imprecise probabilities have important implications for our understanding of decisionmaking and for the design of programs intended to change people's minds.
The Potential and Conditions of the Development of Virtual Reality in Education at the Higher Education Level

The paper explores the potential and determinants for the virtual reality usage in education at the higher education level. The article starts with the analysis of the evolving university’s role. In this context, generational changes determining the VR usage in tertiary education are also discussed. Then the features of virtual reality and related terms are explained to make a basis for discussing VR as a learning aid of the 21st century. The paper closes with the case study analysis of immersive VR usage in higher education and further research recommendations.
Technological, Social and Legal Aspects of the Metaverse in the Light of the Literature Review

In this paper, we have reviewed selected issues related to the metaverse concept, taking into account selected social, technological, and legal aspects. We focused on reviewing the newest literature on the metaverse topic published in the years 2020–2023. The database was gained through Google Scholar search, in order to check if any specific publishers or research areas are dominant among results provided by this search engine. The qualitative content analysis of the literature within the scope of technology, social sciences, and law, revealed the most promising and most threatening aspects of metaverse development and dissemination.
XR Workflows in Film Production: Demonstration for Educational Purposes

The use of the XR production technologies has expanded recently in the film industry and heavily influenced the film production process. This paper aims therefore to explore the differences between diverse XR production workflows. The analysis provides criteria for workflow paths of traditional approach to film production and XR production flow, including workflow structure, skills, education, and equipment. Defined XR techniques are discussed for educational purposes. Its illustrations are three case studies of cinematic VR production, computer-generated VR production and Virtual Production. They are the basis for workflow comparison and discussion on artistic decisions and production challenges for film related Higher Education Institutions (HEI) students and scholars.
Application of wireless sensor network technology based on artificial intelligence in security monitoring system

Abstract The safety monitoring system has been used to monitor and manage engineering safety operation. The application scope of the safety monitoring system is very wide. It has a wide range of applications in the fields of pipeline safety monitoring, electrical safety monitoring and household safety monitoring. This article studied the application process of the household safety monitoring system. Many home safety accidents are caused by inadequate monitoring of safety problems. Therefore, it is very important to establish a household safety monitoring system. Traditional home safety monitoring systems only rely on cameras for safety monitoring, and the traditional home safety monitoring system uses too few sensors. With the continuous development of wireless sensor network (WSN) technology, it is possible to build a sensor node network, but provides real-time information for home security monitoring to the greatest extent. This article compared the home safety monitoring system based on the WSN technology of artificial intelligence (AI) with the traditional home safety monitoring system. The experimental results showed that in the large-scale home environment, the average monitoring accuracy of the traditional home security monitoring system and the home security monitoring system based on the WSN technology of AI was 77.76 and 89.36%, respectively. In the small-scale home environment, the average monitoring accuracy of the traditional home safety monitoring system and the home safety monitoring system based on the WSN technology of AI were 87.63 and 94.43%, respectively. Monitoring accuracy refers to the accuracy of the household safety monitoring system in detecting safety issues. Therefore, the application of the WSN technology based on artificial intelligence to the home safety monitoring system can effectively improve the accuracy of home safety monitoring.
Lord Rama Devotees Algorithm: A New Human-Inspired Metaheuristic Optimization Algorithm

Several Human-Inspired Metaheuristic Optimization Algorithms were proposed in literature. But the concept of Devotees-Inspired Metaheuristic Optimization Algorithms is not yet explored. In this article, Lord Rama Devotees Algorithm (LRDA) is proposed which is a new Devotees-Inspired Metaheuristic Optimization Algorithm.
The effect of virtual reality exposure therapy on focus of attention, self-criticism, and interpretation bias in university students with social anxiety

The present research aims to investigate the effectiveness of virtual reality exposure therapy on the focus of attention, self-criticism, and interpretation bias among university students with social anxiety.The present research was conducted using quasi-experimental research with pre-test and post-test control group design. The participants were university students aged between 10 and 30 years old who had visited Isfahan counseling centers in 1399 HS. Among 53 patients referred to the counseling centers, 30 were randomly selected by convenience sampling and then assigned to an experimental group (15 individuals) and a control group (15 individuals). The experimental group received eight sessions of 30-minute and face-to-face virtual reality-based interventions. In contrast, the control group remained on the waiting list. A structured clinical interview and Social Phobia Inventory (SPIN) were used to select and screen students, and the Focus of Attention Questionnaire (FAQ), Levels of Self-Criticism Scale (LOSC), and revised interpretation bias inventory were applied for the pre-test and the post-test. Then, all the data were analyzed by a statistical analysis of covariance.The results showed that virtual reality exposure therapy significantly reduces the symptoms of the focus of attention, self-criticism, and interpretation bias in the experimental group compared to the control group. (P < 0/01).The research showed that virtual reality exposure therapy reduced the symptoms of social anxiety. Therefore, this method can be applied to reduce some symptoms of social anxiety in university students.
Classification of Binary Combinatorial Metrics Which Admit MacWilliam’s Extension Theorem

The MacWilliams’ Extension Theorem (MET) with respect to a combinatorial metric states that every isomorphism between linear codes that preserves combinatorial weight can be extended to an isometric extension of the whole space. Pinheiro, Machado and Firer 19 proposed the problem of characterizing combinatorial metrics over a finite field with two elements that admit the MET. In this paper, we provide the complete description of such metrics.
GLoCNet: Robust Feature Matching With Global–Local Consistency Network for Remote Sensing Image Registration

Feature matching is a fundamental and critical task for remote sensing image registration. However, numerous outliers (false matches) harm the feature point neighborhood structure due to the view transformation from the camera. Meanwhile, unknown local distortion obscures the distinction between inliers (correct matches) and outliers. To solve these problems, we propose a global-local consistency network (GLoCNet) for feature matching to exclude the interference of outliers under various transformation patterns and provide stable neighborhood support for the similarity metric of feature points. Specifically, a global transformation consistency module is proposed to obtain a neighbor pool by exploiting the compact nature of the inlier distribution under different transformation patterns. In addition, feature points interact with information through the local neighborhood consistency module in center-based graph construction. Finally, the difference between inliers and outliers is increased by dynamically adjusting the upper limitation distance of the outlier and suppressing its effect in the neighborhood. We conducted rich experiments on extensive datasets to verify the effectiveness of the proposed method. The experimental results illustrate that the proposed GLoCNet can effectively handle numerous outliers and achieve satisfactory registration results under various transformation patterns.
Supervision Adaptation Balancing In-Distribution Generalization and Out-of-Distribution Detection

The discrepancy between in-distribution (ID) and out-of-distribution (OOD) samples can lead to distributional vulnerability in deep neural networks, which can subsequently lead to high-confidence predictions for OOD samples. This is mainly due to the absence of OOD samples during training, which fails to constrain the network properly. To tackle this issue, several state-of-the-art methods include adding extra OOD samples to training and assign them with manually-defined labels. However, this practice can introduce unreliable labeling, negatively affecting ID classification. The distributional vulnerability presents a critical challenge for non-IID deep learning, which aims for OOD-tolerant ID classification by balancing ID generalization and OOD detection. In this paper, we introduce a novel supervision adaptation approach to generate adaptive supervision information for OOD samples, making them more compatible with ID samples. First, we measure the dependency between ID samples and their labels using mutual information, revealing that the supervision information can be represented in terms of negative probabilities across all classes. Second, we investigate data correlations between ID and OOD samples by solving a series of binary regression problems, with the goal of refining the supervision information for more distinctly separable ID classes. Our extensive experiments on four advanced network architectures, two ID datasets, and eleven diversified OOD datasets demonstrate the efficacy of our supervision adaptation approach in improving both ID classification and OOD detection capabilities.