Reviewerly/exHarmony huggingface dataset:
Comparative Analysis of Various Machine Learning Algorithms to Detect Cyberbullying on Twitter Dataset

The advent of the digital era has seen the rise of social media as an alternative mode of communication. The usage of social media platforms for facilitating contact between individuals has become widespread. As a direct result of this, conventional modes of communication have been replaced by digital modes, thanks to social media. Because of the growing prevalence of cyberbullying, this digital development on social media platforms is a significant problem that must be addressed. Bullies have various options to harass and threaten individuals in their communities because of the platforms that are already available. It has been argued that a number of different tactics and approaches may be employed to combat cyberbullying via the use of early identification and alerts to locate and/or protect victims of cyberbullying. Methods from the field of machine learning (ML) have seen widespread use in the search for language patterns used by bullies to cause damage to their victims. This research paper analyzes standard supervised learning and ensemble machine learning algorithms. The ensemble technique utilizes random forest (RF) and AdaBoost classifiers, whereas the supervised method uses Gaussian Naive Bayes (GNV), logistic regression (LR), and decision tree (DT). We use the dataset to train and evaluate our binary classification model to classify abusive language as bullying or non-bullying and extract Twitter features using term frequency-inverse document frequency (TF-IDF). Downloaded the dataset from Kaggle. This paper analyzes each machine learning algorithm. Ensemble-supervised algorithms outperformed standard supervised algorithms in the analysis. With a dataset, the random forest classifier performed best with 92% accuracy, while the Naive Bayes classifier performed worst with 62% accuracy.
Fast and Accurate YOLO Framework for Live Object Detection

You Only Look Once (YOLO) is a popular problem-solving time visual perception framework that utilizes an individual autoencoder network to detect entity captured in an image. The key idea behind YOLO is to perform object detection in one forward pass of the network, rather than using a two-stage pipeline as in many other object detection frameworks. The framework functions by segmenting an illustration into a matrix of sections and allocating each unit the responsibility of detecting objects. The network then predicts the envelope and category probabilities for objects within each cell. YOLO uses ConvNet architecture for visual perception. The network takes an image as input and outputs a collection of envelope and category probabilities for objects within the visual representation. YOLO has proven to be effective in real-time object detection and has found extensive usage in various domains. However; it has some limitations, such as a lower accuracy compared to other frameworks and difficulty detecting smaller objects. Despite these limitations, YOLO remains a popular choice for real-time object detection due to its efficiency and speed.
Decoding the Twitter Sentiment Using Artificial Intelligence Tools: A Study on Tokyo Olympics 2020

Using techniques such as machine learning (ML), natural language processing (NLP), data mining, and artificial intelligence (AI), sentiment analysis mines, extracts, and categorises user opinions provided on a business, product, person, service, event, or idea. The researchers utilised this emerging strategy to determine how social media users opinion on Tokyo 2022 Olympic and Paralympic Games. The main focus is on the fact that the Olympic Games in 2020 are significant when compared to other major international sporting events since they will be the first major gaming tournament to be staged after the pandemic. In this study, a novel approach known as content analysis and topic analysis were utilised. The main dataset utilised for this study is the tweets that used the hashtag #tokyo2022, the dataset composes of the data collected over the period of a week prior to the event. In total, 18,000 tweets were used. Here, a machine learning model is developed with the help of the tweepy.py module of the Python programming language. To collect data, the strategy of topic analysis was utilised, and the implementation of the AI technique is made possible by using the Twitter API. The collected data is then analysed by NVIVO AI to determine its theme, the polarity of its sentiment, and the frequency of its words. According to the data, there has been a favourable development in the situation and may assume that this is because of the pandemic caused by COVID-19.
Using multisensory virtual reality nature immersion as a therapeutic modality for improving HRV and cognitive functions in post-traumatic stress disorder: a pilot-study

Introduction: Immersive virtual reality (VR) applications are burgeoning within healthcare as they promote high levels of engagement. Notwithstanding, existing solutions only stimulate two of our five senses (audio and visual), thus may not be optimal in the sense of promoting immersion and of “being present”. In this paper, we explore the benefits of an immersive multisensory experience as a therapeutic modality for participants suffering from post-traumatic stress disorder (PTSD). Methods: In addition to 360-degree videos and corresponding natural sounds, nature smells are also presented by means of a portable ION 2 scent diffusion device attached to an Oculus Quest 2 VR head-mounted display. A 3-week 12-sessions protocol was applied to a sample of 20 participants diagnosed with PTSD. Results and discussion: We report the outcomes seen from a battery of qualitative metrics, including cognitive functioning tests, psychological symptoms, severity of PTSD, and several self-reported questionnaires and heart rate variability (HRV) metrics. Results are compared not only between pre-and post intervention, but also after a 3-month follow-up period. Results suggest a decrease in the severity of PTSD, as well as improvements in processing speed and sustained attention post-intervention, but also sustained decrease in the severity of PTSD and in dissociative tendencies at the 3-month follow-up. Overall, participants rated the experience as highly immersive and produced very mild to no symptoms of cybersickness, thus corroborating the feasibility and usefulness of the proposed multisensory immersive VR tool for reducing PTSD symptoms.
A localization method of manipulator towards achieving more precision control

Abstract The monocular vision system is a crucial branch of machine vision research widely used in multiple industries as a research hotspot in the field of vision. Although the monocular vision system is of simple structure and cost‐effectiveness, its positioning accuracy is insufficient in some industries. This article researched the robot arm positioning method via monocular vision. First, we built a vision system model and designed the style of a cooperative target for target positioning. Second, a target feature screening method based on conditions is composed for the existence of interference. Furthermore, combining the principle of pose estimation on the PNP (Perspective‐n‐Point) problem with the results of the visual system calibration to realize the positioning of the target. Finally, complete the construction of the experimental platform and design accuracy evaluation experiments and positioning experiments. The experimental results show that the location measurement error range of the system in this article is below 4 mm, and the measurement error of the rotation angle is below 2. The system can adapt to the requirements of general industrial use.
Quantum-enhanced secure-link architecture with quantum stream cipher Y-00 and post-quantum cryptography: basic structure and experiment

This paper considers an application of the quantum stream cipher (QSC) Y-00. QSC Y-00 can provide secure long-haul and high-capacity data transmission in optical networks. Post-quantum cryptography (PQC) can provide quantum-resistant authentication and key-establishment. We propose a secure link architecture for optical communication networks using QSC Y-00 and PQC. Further, we report a field experiment using the optical fibers embedded along a railway truck between two train stations to demonstrate user authentication, key-establishment, and secure data communication with the intensity modulation-based Y-00 transceivers and PQC. Finally, we discuss the further applications of the proposed secure link architecture.
A novel enhanced flow regime algorithm using opposition-based learning

Metaheuristics are widely used in science and industry because it as a high-level heuristic technique can provide robust or advanced solutions compared to classical search algorithms. Flow Regime Algorithm is a novel physics-based optimization approach recently proposed, and it is one of the candidate algorithms for solving complex optimization problems because of its few parameter configurations, simple coding, and good performance. However, the population that initialized randomly may have poor diversity issues, resulting in insufficient global search, and premature convergence to local optimum. To solve this problem, in this paper, a novel enhanced Flow Regime Algorithm based on opposition learning scheme is proposed. The proposed algorithm introduces the opposition-based learning strategy into the generation of some populations to enhance the global search performance while maintaining a fast convergence rate. In order to verify the performance of the proposed algorithm, 23 benchmark numerical optimization functions were studied experimentally in detail and compared with six well-known algorithms. Experimental results show that the proposed algorithm outperforms all other metaheuristic algorithms in all unimodal functions with higher accuracy, and can obtain competitive results on more multimodal cases. A statistical comparison shows that the proposed algorithm has superiority. Finally, that the proposed algorithm can achieve higher quality alignment compared to most other metaheuristic-based systems and OAEI ontology alignment systems.
An identity management scheme for cloud computing: Review, challenges, and future directions

Cloud computing is a cost-effective way for organizations to access and use IT resources. However, it also exposes data to security threats. Authentication and authorization are crucial components of access control that prevent unauthorized access to cloud services. Organizations are turning to identity management solutions to help IT administrators face and mitigate security concerns. Identity management (IDM) has been recognized as a more robust solution for validating and maintaining digital identities. Identity management (IDM) is a key security mechanism for cloud computing that helps to ensure that only authorized users have access to data and resources. Traditional IDM solutions are centralized and rely on a single authority to manage user identities, which makes them vulnerable to attack. However, existing identity management solutions need to be more secure and trustworthy. Blockchain technology can create a more secure and trustworthy cloud transaction environment. Purpose: This paper investigates the security and trustworthiness of existing identity management solutions in cloud computing. Comparative results: We compared 14 traditional IDM schemes in cloud systems to explore contributions and limitations. This paper also compared 17 centralized, decentralized, and federated IDM models to explain their functions, roles, performance, contribution, primary metrics, and target attacks. About 17 IDM models have also been compared to explore their efficiency, overhead consumption, effectiveness to malicious users, trustworthiness, throughput, and privacy. Major conclusions: Blockchain technology has the potential to make cloud transactions more secure and reliable. It featured strong authentication and authorization mechanisms based on smart contracts on the Ethereum platform. As a result, it is still regarded as a reliable and immutable solution for protecting data sharing between entities in peer-to-peer networks. However, there is still a large gap between the theoretical method and its practical application. This paper also helps other scholars in the field discover issues and solutions and make suggestions for future research.
Knowledge and Awareness of Leukemia Among the Population of Eastern Province, Saudi Arabia

Cancer is characterized by the rapid proliferation of abnormal cells that exceed their normal boundaries, infiltrating other body parts and leading to metastasis, a distinctive feature setting it apart from other diseases. Metastasis is the primary cause of cancer-related deaths, with nearly 10 million global fatalities, making it the leading cause of mortality. Leukemia, a type of cancer originating in the bone marrow or blood cells, presents significant complications and is associated with various risk factors, including a positive family history, smoking, and obesity. This study aims to evaluate the general knowledge of leukemia and its associated risks among the inhabitants of the Eastern Province, Saudi Arabia.A cross-sectional survey was conducted targeting all residents of the Eastern Province, Saudi Arabia, who were Saudi nationals, spanning both genders and aged 15 to 59 years. The questionnaire was distributed electronically through social networking applications, and responses were collected via Google Forms (Google, Mountain View, CA).The study findings indicated that the most frequently identified risk factors for leukemia included blood disorders, genetic disorders, and extensive exposure to radiation and chemicals. The most prevalent leukemia symptoms were bruising and bleeding, followed by vomiting, nausea, and headache. The most commonly reported side effects of leukemia treatment were dizziness, followed by anorexia and exhaustion.The study revealed a lack of awareness about leukemia and its associated risks among participants. This underscores the need for continued educational initiatives and awareness campaigns to improve leukemia knowledge and early detection rates, potentially leading to better outcomes for affected individuals. Future research should aim to overcome study limitations and provide a broader perspective on leukemia awareness throughout Saudi Arabia.
Identification over quantum broadcast channels

Abstract Identification over quantum broadcast channels is considered. As opposed to the information transmission task, the decoder only identifies whether a message of his choosing was sent or not. This relaxation allows for a double-exponential code size. An achievable identification region is derived for a quantum broadcast channel, and a full characterization for the class of classical-quantum broadcast channels. The identification capacity region of the single-mode pure-loss bosonic broadcast channel is obtained as a consequence. Furthermore, the results are demonstrated for the quantum erasure broadcast channel, where our region is suboptimal, but improves on the best previously known bounds.
Phase retrieval for dual-projection pattern based on orthogonal frequency encoding to suppress superposing effects

When grating patterns are simultaneously projected by a dual-projection structured-light system, interference-like blur and brightness overexposure in the superposed area often cause miscalculation of the phase of the grating pattern. In this study, we proposed a novel method, to the best of our knowledge, that utilizes orthogonal grating encoding to retrieve the phases of superposed grating patterns. Specifically, we determined the frequency of the dual-projection pattern based on the condition that enabled the separation of superposed orthogonal signals in wireless communication. Additionally, the maximum intensity of the projected pattern was determined using the intensity-saturation relationship. By performing a discrete Fourier transform on a series of superposed grating patterns, we obtained the wrapped phase of the corresponding projected grating patterns in the space-time dimension. Finally, we reconstructed the measured object by fusing the point clouds obtained from the dual-projection structured-light system. The experimental results demonstrated that the encoded orthogonal grating patterns could eliminate interference-like blurring and brightness overexposure during superposition and obtain high-precision phase maps and 3D reconstruction results, which provides the possibility for the simultaneous reconstruction of multiprojection structured light.
Pengaruh Kompetensi Sumber Daya Manusia, Whistleblowing System, Moralitas Individu, Dan Sistem Pengendalian Internal Terhadap Pencegahan Fraud Dalam Pengelolaan Dana BUMDes di Kabupaten Sidoarjo

Prevention fraud is a form of action with the aim of reducing the opportunity for dishonesty and trying to tackle activities that create the risk of fraud. This research was conducted with a focus on the factors for preventing fraud in the management of BUMDes funds in Sidoarjo Regency, namely the competence factor of HR, the Whistleblowing System, individual morality factors, and internal control system factors. The study used quantitative methods to analyze primary data from the questionnaire results. The subjects of this study were the chairman and management of BUMDes in Sidoarjo Regency, totaling 76 respondents. The sampling technique used is random sampling and the sample calculation used is the Slovin formula. Analysis of the data used is the partial t-test Hypothesis Test on the IBM SPSS 26 software. The results show several factors that have an influence on fraud , namely HR competence (Human Resources), and Whistleblowing System, then individual morality. Meanwhile, the factor of the internal control system as a factor that does not affect the prevention of fraud.
Probabilistic Neural Network Application to Speech Recognition Problem

&lt;p&gt;This article discusses an approach to solving the problem of speech recognition based on probabilistic neural networks. The problem is formulated as a problem of command recognition. Commands have equal lengths (in words). Each word position has its own set of candidates. The recognition algorithm for solving this problem was developed. The core of the algorithm is a probabilistic network that recognizes modified estimates of spectral densities. The algorithm allows for high precision of recognition, which is sufficient for the creation of a voice user interface.&lt;/p&gt;
The effect of e-government development indices (EGDI) on corruption perception index in sub-Sahara Africa: A panel data analysis

AbstractThe study aims to examine the impact of e-government development indices on the corruption perception index (CPI) using cross-sectional data covering the period 2012–2020. To achieve the objective of the study, the E-Government Development Index (EGDI), which comprises the Online Service Index (OSI), the Human Capital Index (HCI) and the Telecommunication Infrastructure Index (TII) was utilized. The study adopted the panel data statistical methodology to accomplish the goal of the study. According to the findings, perceptions of corruption rise with an increase in the OSI, the TII has a statistically non-negative significant relationship with perceptions of corruption, and the HCI decreases perceptions of corruption. This portends that e-government positively impacts online services, improved telecommunication infrastructure, and the development of human capital in sub-Saharan Africa, and as well has a positive impact on how people perceive corruption. Moreso, an increase in human capacity by providing adequate training, conferences, workshops, and other activities will result in a decrease in the corruption index across the 48 sub-Saharan Africa countries. Based on the findings, the study suggests that improved internet service can stimulate improved efficiency in the perception of corruption, resulting in a more efficient and effective elimination of corruption in African nations.Keywords: e-governancecorruptionelectronic government development index (EGDI)Africa, sub-Saharanpanel data AcknowledgmentsThe authors appreciate the effort of everyone that contributed to this study, as well as the anonymous reviewers and editors for their constructive input in this manuscript.Disclosure statementNo potential conflict of interest was reported by the authors.
Regulatory policy uncertainty, banking industry innovations and financial development among emerging markets

Purpose This study examines the extent to which regulatory policy uncertainty, macroeconomic risk, banking industry innovations, etc. influence variability in financial sector development among emerging economies in sub-Sahara Africa (SSA). Design/methodology/approach Data for the empirical inquiry were compiled from a sample of 25 economies from the subregion from 2010 to 2020. Empirical estimates examining the relationships noted above were carried out using the two-step system generalized method of moments estimation technique. Findings Results the empirical estimates suggest that regulatory policy uncertainty and macroeconomic risk adversely influence or constrain financial sector development among the economies examined in the study. Banking industry innovations on the other hand is found to positively influence the development of the financial sector in these economies. Furthermore, moderating empirical analysis suggests that effective governance positively moderates the relationship between banking industry innovations and financial development among economies in the subregion. Originality/value This study’s approach to the mechanics of financial development among economies in SSA is designed to offer different perspectives to those found in the existing literature on financial development in three fundamental ways. First, although the verification of the role of banking industry innovations in financial development may not be new, it is important to point out that the approach used in this study is based on an index for innovations with different constituents or principal components in its construction; making the variable significantly different from what has been examined in the literature. In addition, the review of regulatory policy uncertainty and macroeconomic risk (both variables are multifaceted constructs using the principal component analysis procedure) further brings into this study’s analysis, a different approach to examining conditions influencing variability in financial development among developing economies.
Implementation of a Distributed Electronic Voting System Using a Blockchain-Based Framework

The authority claims that an electronic voting system is secure, yet features such as transparency and integrity are not adequately addressed. This study focuses on developing an online voting platform that uses Blockchain technology to meet the aforementioned characteristics and thereby take account of its advantages as it operates as a service to conduct voting and store the results.
A Conceptual Model for the Role of Blockchain in Overcoming Supply Chain Challenges

Blockchain is an emerging and disruptive technology that has gained growing popularity in recent years and has been implemented in different fields. This chapter reviews the Blockchain adoption in the supply chain domain by examining the achievements of Blockchain in overcoming supply chain difficulties. The majority of prior research is predominantly about possible ways of adoption, proofs-of-concept, and beta projects. This research goes a step further and describes the effects of implementing Blockchain-based applications in real supply chain cases. The findings are achieved with a multiple case-study analysis on a sample of 10 projects selected out of 45 Blockchain-based supply chain projects. Finally, a conceptual model incorporating the formulated propositions is presented, suggesting that Blockchain has more potential to address enhanced collaboration, transparency, sustainability, risks mitigation, and efficiency challenges of supply chains.