Reviewerly/exHarmony huggingface dataset:
Autonomus Call System Berbasis ESP32 Untuk Peringatan Dini Kebakaran Rumah

Fires can occur anywhere, both in buildings and in residential areas. Fires often occur due to human negligence caused by many factors, such as large or small LPG cylinder leaks, the danger of disposing of cigarette butts, and electrical short circuits, causing fire and spreading to other parts. The problem that often arises so far is that the information received about the location of the fire is often wrong, so the fire department arrives late to the actual fire location, therefore a tool is needed to provide an early warning system for house fires. Based on the above problems, we propose a tool that can make calls automatically to provide accurate fire information such as address coordinates to firefighters and early warning of gas leaks to homeowners. This tool uses the ESP32 as a microcontroller, a fire sensor, and MQ2 as the main sensor, Thiger.IO monitors data sensors, telegram messages, email notifications, and phone calls using SIM 800L. The results of this tool can detect gas leaks when gas levels greater than 400 PPM, and a fire occurs with a maximum fire detection distance of 250 cm. Telegram notifications sent in real-time to homeowners, phone calls, and emails of fire address information to the firefighter worked great. The average response time for telephone calls is 6.59 seconds, and a success percentage of 100% obtained in making telephone calls with stable signal conditions.
Applying Deep Learning for Morphological Analysis in the Sinhala Language

This research was performed for analyzing morphology of the Sinhala language. Six different deep learning architectures, including RNN, LSTM, and GRU, with and without bidirectional processing was used in the study. Two different datasets, in both Sinhala and Roman scripts, were considered, with each dataset consisting of a total of 644k unique entries. The results were compared to identify the best-performing architecture. Among all the approaches, the model trained with the Sinhala script dataset using bidirectional Gated Recurrent Unit (BiGRU) as the deep learning architecture provided the highest accuracy (87.96%). Several other experiments, such as predicting morphemes and definitions separately, were also considered to assess the behavior of deep learning in morphological analysis in the Sinhala language. All these experiments yielded more than 88% accuracy. These positive results demonstrate the promising potential of deep learning approaches for morphological analysis in the Sinhala language. Using the best performing model, we developed an application for users who are interested in learning and analyzing Sinhala words and their morphology.
Enhancing Tourists’ Satisfaction Through Blockchain Technology

Background: Blockchain technology, a decentralized ledger system, offers a secure and transparent means to record transactions, holding promise for enhancing tourists' contentment within the travel industry. Objective: The aim of the study was to assess how blockchain technology impacts tourists' trust, willingness to pay, and environmental considerations. Methodology: Employing a qualitative research approach combined with thematic analysis, the study was conducted. The research was conducted in the Kolkata region of West Bengal. Sample size was 18 participants who were tourists utilizing travel agency services for travel purposes. Data was collected by semi-structured interview. Thematic was utilized to analyze the interview data. Results: The research unveiled that respondent possessed a solid comprehension of blockchain technology and recognized its potential effects on the travel sector. Their preference leaned towards travel-related products and services utilizing blockchain, driven by the belief in heightened security, efficiency, and cost-effectiveness. Furthermore, respondents expressed a willingness to invest more in travel offerings incorporating blockchain technology, assuming it would deliver superior security, transparency, efficiency, sustainability, and innovation. Conclusion: The study's outcomes affirm that blockchain technology holds the potential to significantly enhance tourists' satisfaction within the travel industry. Nonetheless, further investigation is required to explore the precise mechanisms by which blockchain can elevate tourists' experiences.
A Study On Diners’ Opinion Towards Induction Of Service Robots In Gujarat Restaurants

Background: The rapid development of technology has allowed the restaurant sector to integrate a wide range of technologies into service settings. Service robots can boost efficiency and food service revenues. This study examines consumer approval of diner service robots. Objectives: (a) To analyse diners' views on restaurant robots, (b) To know the effect of demographic considerations on robot introduction in restaurants and (c) To examines diners views on prospects and difficulties that affect service robot attitudes. Methodology: An exploratory research design with structured questionnaire was distributed to 362 participants among which 337 were suitable for the study. Purposive sampling was used to obtain diner’s perspective of inducting service robots in restaurants at major cities of Gujarat (Ahmedabad, Surat, Baroda, Gandhinagar &amp; Rajkot). Data analysis was done on the basis of demographic profile and frequency of visit to the restaurants by using SPSS 25.0. Results: Based on admissible Eigen values, the study derived three criteria for diner acceptance of service robots that explained 72.28% of variance. Hypotheses showed age and gender influenced service robot induction. Education did not affect service robot acceptance. The study suggests that people's views on robots affect their views on restaurant robots. Conclusion: The study shows that service robots could assist restaurants. It specifies service robots' study operations. Diner acceptance of service robots will give a theoretical framework for other service industries in technological acceptance and consumer behavior.
Racism and Hate Speech Detection on Twitter: A QAHA-Based Hybrid Deep Learning Approach Using LSTM-CNN

Twitter, a predominant platform for instantaneous communication and idea dissemination, is often exploited by cybercriminals for victim harassment through sexism, racism, hate speech, and trolling using pseudony-mous accounts. The propagation of racially charged online discourse poses significant threats to the social, political, and cultural fabric of many societies. Monitoring and prompt eradication of such content from social media, a breeding ground for racist ideologies, are imperative. This study introduces an advanced hybrid forecasting model, utilizing convolutional neural networks (CNNs) and long-short-term memory (LSTM) neural networks, for the efficient and accurate detection of racist and hate speech in English on Twitter. Unlabelled tweets, collated via the Twitter API, formed the basis of the initial investigation. Feature vectors were extracted from these tweets using the TF-IDF (Term Frequency-Inverse Document Frequency) feature extraction technique. This research contrasts the proposed model with existing intelligent classification algorithms in supervised learning. The HateMotiv corpus, a publicly available dataset annotated with types of hate crimes and ideological motivations, was employed, emphasizing Twitter as the primary social media context. A novel aspect of this study is the introduction of a revised artificial hummingbird algorithm (AHA), supplemented by quantum-based optimization (QBO). This quantum-based artificial hummingbird algorithm (QAHA) aims to augment exploration capabilities and reveal potential solution spaces. Employing QAHA resulted in a detection accuracy of approximately 98%, compared to 95.97% without its application. The study's principal contribution lies in the significant advancements achieved in the field of racism and hate speech detection in English through the application of hybrid deep learning methodologies.
Human Resource Information Systems Information Security and Organizational Performance of Commercial State Corporations in Kenya

The purpose of this study was to establish the influence of HRIS information security on the organisational performance of commercial state corporations in Kenya. A Cross-sectional descriptive research design was used. The population was drawn from 55 Commercial State Corporations in Kenya. The units of observation were the managing directors or chief executive officers, directors of human resources, and deputy directors of human resources. The study used qualitative and quantitative methods to collect primary and secondary data. Data were collected from 110 respondents. Questionnaires and interviews were used to collect data for the study, supplemented by secondary sources. SPSS version 23.0 was used to analyse data using descriptive analysis, factor analysis, Pearson correlation, analysis of variance (ANOVA), and regression. The correlation and regression results revealed that HRIS Information Security had a positive and significant relationship with Kenya's Organizational Performance of Commercial State Corporations. The null hypothesis indicating no significant relationship between HRIS Information Security and Organisational performance of Commercial State Corporations in Kenya was rejected. From the study findings, it was recommended that organisations should keep a record of who is in charge of implementing the security agenda across the board and let all employees know about it. Information security policies, organisational internet usage policies, software management policies, and security expectations that align with the functions' tasks should all be explained to and covered in the training for employees
An Access Control System using RFID and Face Verification with QR Code-based Enrollment

The objective of this paper is to propose the system architecture and implementation of an access control system using RFID technology and face verification. The implemented system was employed to control user’s access at the tripod turnstile gate in the library of the Prince of Songkla University (Trang Campus). The access control system is divided into two parts: enrolment and access control. In the enrolment part, the system obtains the user information from three processes: (1) the basic user information (e.g., full name and university identification number) is received via a SOAP web service,(2) the face image of a user is detected by using the camera of a mobile phone, (3) the UID data of a RFID card is read in a form of QR code by using our enrolment board. In the access control part, the RFID reader board forwards the UID data reading from an RFID card into the main controller board via RS-232 communication. The main controller board is used to send the UID data into the Flask web server in order to retrieve the corresponding face image encoding, and then verify with faces that are detected from the webcam at the tripod turnstile gate. The main controller board also controls the tripod turnstile gate via the push-pull solenoid and receives external signals from the proximity sensor and limit switch. The performance of the implemented system using the Raspberry Pi 4 Model B board as the main controller board in terms of verification accuracy and response time was measured, and also compared with the NVIDIA Jetson Nano board.
Matlab sımulatıon of quadcopter dynamıcs and PID attıtude controller

Due to the ever-growing popularity of quadcopters, they are used in various fields such as surveillance, military, surveillance and courier services and are of great importance. Ideal handling of quadcopters is essential for safe maneuvering and high precision flight performance. This research provides simulation of quadcopter control in MATLAB software, development and review of various control principles.&#x0D; Dynamic development in unmanned aerial vehicles (UAVs) has led to the rapid spread of quadcopters in many fields, from airspace control to courier services. This research study investigates the Proportional-Integral-Derivative (PID) attitude controller by simulating the quadcopter dynamic object in MATLAB software. The heart of this research is to create and develop a PID attitude controller, which is a critical component for accurate control of quadcopter.
Bibliometrical Analysis in Multimedia Jurnal Face Recognition

The objective of this research is to bibliometrically analyze multimedia journals on the subject of “face recognition” with a focus on developments and related research trends over a certain period of time. The research uses bibliometric methodologies to identify and analyze scientific publications related to face recognition in the multimedia world, including trends in research, major contributors, the most frequently quoted journal, and how research topics evolve over time. These findings provide an important understanding of how multimedia face reconnaissance evolves and develops, and provide a solid foundation for further research in this field. Thus, this research can be an important reference for researchers, practitioners, and decision makers in the fields of artificial intelligence, facial recognition, multimedia technology in general.
Execution of Serverless Functions Lambda in AWS Serverless Environment

Serverless computing, epitomized by AWS Lambda, has revolutionized application development and deployment by abstracting away server management and offering auto-scalability. AWS Lambda's 15-minute maximum timeout for function execution, however, presents a unique challenge for users aiming to process longer tasks within the serverless framework. In this paper, we dive deep into the execution dynamics of AWS Lambda within the AWS serverless environment, with a particular focus on extended execution times. As serverless computing gains traction, understanding the nuances and limitations of AWS Lambda's default settings becomes paramount.&#x0D; This paper investigates different request handling mechanisms within AWS Lambda and presents empirical data to demystify the underlying processes. By addressing the challenges posed by long-running serverless functions, we aim to provide practical insights and potential solutions for developers and architects seeking to optimize their serverless applications. In the quest for efficient triggering mechanisms for extended serverless functions, this paper offers valuable guidance, empowering users to leverage AWS Lambda effectively while circumventing the default timeout constraints.
Patient Health Care Opinion Systems using Ensemble Learning

The patients’ experience is considered a dominant reputation in the hospital administration and medical fields. Online patient reviews are recognized as an important criterion for evaluating hospital service quality and performance. The classical approach of evaluating service excellence is often found to be tedious. But with machine learning classifiers and opinion mining techniques the data assessing, and evaluation is made casual and its saves time. Currently, patient satisfaction and quality of service for patients in hospitals plays a major role in health care sector. In this paper a novel Ensemble Model is proposed to Analyze Patient Health Care Opinion Systems. The Classification models are used to classify patients’ feelings as positive, negative, or neutral using a machine learning approach to predict superlative models in data analysis. Ensemble techniques are used to analyze the opinions classified by the model, and the recommendation for health care is analyzed based on sentiment polarity.
Optimizing University Mobility : An Internal Navigation and Crowd Management System

In the evolving landscape of educational technology, the article explores the critical frontier of indoor navigation systems, focusing on universities. Traditional approaches in higher education often fall short of meeting dynamic user expectations, necessitating revolutionary solutions. This research introduces an innovative internal navigation and crowd management system that seamlessly integrates augmented reality, natural language processing, machine learning, and image processing technologies. The Android platform serves as the foundation, harnessing augmented reality's transformative capabilities to provide real-time visual cues and personalized wayfinding experiences. The voice interaction module, backed by NLP and ML, creates an intelligent, context-aware assistant. The crowd management module, employing advanced image processing, delivers real-time crowd density insights. Personalized recommendations, powered by NLP and ML, offer tailored canteen suggestions based on user preferences. The agmented reality navigation module, using Mapbox, Unity Hub, AR Core, and Vuforia, enriches the user experience with dynamic visual cues. Results reveal the success of each module: the voice interaction module showcases continuous learning, user-centric feedback, contextual guidance excellence, robust security, and multimodal interaction flexibility. The crowd management module excels in video feed processing, image processing with OpenCV, and real-time availability information retrieval. The personalized recommendations module demonstrates high accuracy, equilibrium, and robust performance. The AR navigation module impresses with precision, enriched navigation, and tailored routes through machine learning. This cohesive system sets new benchmarks for user-centric technology in universities. Future work includes multi-university integration, intelligent spatial design, and real-time decision support, paving the way for more efficient, user-centered university experiences and contributing to the advancement of smart university environments. The research serves as a pivotal force in reshaping interactions within university spaces, envisioning a future where technology seamlessly enhances the essence of human interaction in educational environments.
A Comprehensive Survey on Deepfake Methods: Generation, Detection, and Applications

Due to recent advancements in AI and deep learning, several methods and tools for multimedia transformation, known as deepfake, have emerged. A deepfake is a synthetic media where a person's resemblance is used to substitute their presence in an already-existing image or video. Deepfakes have both positive and negative implications. They can be used in politics to simulate events or speeches, in translation to provide natural-sounding translations, in education for virtual experiences, and in entertainment for realistic special effects. The emergence of deepfake face forgery on the internet has raised significant societal concerns. As a result, detecting these forgeries has become an emerging field of research, and many deepfake detection methods have been proposed. This paper has introduced deepfakes and explained the different types of deepfakes that exist. It also explains a summary of various deep fake generation techniques, both traditional and AI detection techniques. Datasets used for deepfake-generating that are freely accessible are emphasized. To further advance the deepfake research field, we aim to provide relevant research findings, identify existing gaps, and propose emerging trends for future study.
Breast Tumor Recognition by Semantic Segmentation of Multiclass Ultrasound Images

Objectives: The main purpose of this paper is to suggest a semantic segmentation model to reduce training time in ultrasound breast cancer images. This is achieved by employing a smaller network with fewer trainable parameters, resulting in faster training while maintaining maximum accuracy.&#x0D; Methods: This paper proposes a modified U-Net model, which we call the V model, for the subdivision of breast tumors. The proposed V architecture is applied explicitly to ultrasound breast cancer datasets for semantic segmentation. Our proposed model achieves semantic segmentation by employing an encoder and decoder on real and mask image datasets.&#x0D; Findings: Therefore, developing a proposed system, namely a V-Net computer-aided diagnosis (CAD) system, is imperative. This CAD system aims to minimize human errors while enhancing accuracy and speed in the premature finding of breast tumors. The proposed model utilizes minimal layers and parameters while maintaining superior results regarding correctness, speed, and computational proficiency.&#x0D; Novelty: The proposed V-net model applies to analysing any medical image for detecting disease and finding more accuracy than other U-net models.
Securing Cloud Data: An Enhanced Approach through Attribute-Based Access Control Mechanism

Cloud computing is considered one of the most dominant paradigms in the Information Technology (IT) industry these days. It offers new cost-effective services on-demand such as Software as a Service (SaaS), Infrastructure as a Service (IaaS) and Platform as a Service (PaaS). However, with all of these services promising facilities and benefits, there are still a number of challenges associated with utilizing cloud computing such as data security, abuse of cloud services, malicious insider and cyber-attacks. Among all security requirements of cloud computing, access control is one of the fundamental requirements in order to avoid unauthorized access to systems and protect organizations assets. Although, various access control models and policies have been developed such as Attribute based Access Control (ABAC) and Role Based Access Control (RBAC) for different environments, these models may not fulfil cloud’s access control requirements. In this paper, an enhanced attribute-based access control based on strategy mechanism is proposed by introducing a strategy for providing access control to the users in the cloud environment.
Collaborative Landscape and Knowledge Dynamics in IoT and Blockchain Research: A Bibliometric Analysis

This research article presents a bibliometric analysis of the intersection between Internet of Things (IoT) and Blockchain, utilizing data sourced from the Web of Science database. The analysis employs the VOSviewer tool to investigate cocitation, co-occurrence, and bibliometric correlations among the publications. The findings indicate a surge in publications, with the highest number recorded in 2022, reflecting the growing interest and significance of this research area. Moreover, the analysis reveals China as the leading country in terms of research output, underscoring its active involvement in IoT and Blockchain research. Additionally, the study identifies IEEE Access as the leading publisher, indicating its prominence in disseminating relevant research. Notably, the analysis of keywords reveals "blockchain" as the most frequently occurring term, highlighting its central role in the field. These findings contribute to a better understanding of the research landscape in IoT and Blockchain, providing insights into collaboration patterns, influential countries, leading publishers, and the prominence of blockchain as a research topic.